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Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, a factor that should be considered by decision-makers. We focus on terrorism as a mostly endogenously driven form of political uncertainty and assess the forecasting performance of market-based and professional inflation and exchange rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, the performance of both market-based and expert forecasts is significantly worse during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange rate forecasts.
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Forecast Performance in Times of
Jonathan Benchimolyand Makram El-Shagiz
September 8, 2020
Governments, central banks, and private companies make extensive use
of expert and market-based forecasts in their decision-making processes.
These forecasts can be a¤ected by terrorism, a factor that should be consid-
ered by decision-makers. We focus on terrorism as a mostly endogenously
driven form of political uncertainty and assess the forecasting performance
of market-based and professional in‡ation and exchange rate forecasts in
Israel. We show that expert forecasts are better than market-based fore-
casts, particularly during periods of terrorism. However, the performance
of both market-based and expert forecasts is signi…cantly worse during such
periods. Thus, policymakers should be particularly attentive to terrorism
when considering in‡ation and exchange rate forecasts.
Keywords: in‡ation, exchange rate, forecast performance, terrorism,
market forecast, expert forecast.
JEL Classi…cation: C53, E37, F37, F51.
Please cite this paper as:
Benchimol, J., and El-Shagi, M., 2020. Forecast performance in times
terrorism. Economic Modelling, 91, 386-402.
This paper does not necessarily re‡ect the views of the Bank of Israel. The authors thank
the associate editor, Joakim Westerlund, the anonymous referees, Itamar Caspi, Wagner Piazza
Gaglianone, Dan Galai, Eleonora Granziera, Rudy Malka, Ariel Mansura, Benzion Schreiber,
Yoav So¤er, Michel Strawczynski, Harald Uhlig, Noam Zussman, and participants at the 34th
Israel Economic Association, 49th Money, Macro and Finance Research Group, and 34th CIRET
annual conferences, as well as participants of the Romanian Academy, University of Macau and
Bank of Israel’s research seminars for their useful comments.
yBank of Israel, Jerusalem, Israel. Email:
zCenter for Financial Development and Stability, Henan University, Kaifeng, China, and
Halle Institute for Economic Research (IWH). Corresponding author. Email: makram.el-
1 Introduction
In recent years, more and more researchers have shown interest in the economic
consequences of terrorism, fuelled by the increase in terrorist attacks in highly
developed countries that have traditionally been considered safe.1
Although this has led to greater understanding of the real economic impact of
terrorism, little attention has been paid to the impact of terrorism on expectations.
Given that terrorism by its very de…nition aims to "intimidate or create panic",
omission of the more direct psychological impact that can be seen in the expec-
tations seems problematic.2Expectations play a pivotal role in the mechanism of
modern macroeconomic models. Central banks, other policy makers, and other
public and private institutions rely heavily on professional forecasts and market-
implied expectations in their decision-making. Understanding how a climate of
fear generated by terrorism a¤ects those expectations and forecasts is useful for
proper policy making.
This study aims to …ll this gap by using data on Israel, the developed country
that has been by far the greatest target of terrorist activity (per capita). There
seems to be consensus in the literature that rare and infrequent terrorist attacks3
have a limited direct and immediate economic impact on developed economies
(Abadie and Gardeazabal, 2003).
However, persistent terrorism, as observed in Israel, might have a di¤erent
ect.4Eckstein and Tsiddon (2004) show that in the absence of (regular) terrorist
attacks, such as the Second Intifada, Israel’s per capita GDP would be higher than
its actual GDP. For example, home prices in Israel are lower in regions that are
more prone to terrorist attacks (Elster et al., 2017). Fielding (2003a) demonstrates
that the First Intifada— which he interprets as a measure of political uncertainty—
contributed substantially to Israel’s low rate of investment, and Fielding (2003b)
1See, for example, Blomberg et al. (2004), Crain and Crain (2006), Dorsett (2013), Gerlach
and Yook (2016), and Ruiz Estrada and Koutronas (2016) among others.
2For instance, Wallace and Wild (2010) de…nes terrorism as “the threat or actual use of
violence in order to intimidate or create panic, especially when utilized as a means of attempting
to in‡uence political conduct.”
3It is important to distinguish di¤erent types of terrorism: frequent at low intensity (Israel
during the last decade), frequent at high intensity (Iraq and Syria), rare at low intensity (the
United States during the last decade), and rare at high intensity (the United States on 9/11, the
United Kingdom during the London subway bombings, and France at Paris and Nice).
4Although our paper focuses on Israel, there are some general implications. It has been argued
that the impact of terrorism in the West is di¤erent than it is in Israel because of the profound
di¤erence in the nature of terrorism in these countries. As terrorism is more frequent in Israel, it
can be anticipated and modeled, while terrorist attacks remain profoundly unpredictable (black
swan) in other Western countries. However, in recent years this is no longer true, as the Global
Terrorism Index readings of France, the United States, and the United Kingdom (Institute for
Economics and Peace) have become very close to that of Israel. Moreover, fatalities caused by a
single terrorist attack have been higher in European countries than in Israel over the last decade.
Therefore, we explicitly distinguish between the frequency and magnitude of terrorist attacks.
Other di¤erences also, of course, remain. Most importantly, Israel is much smaller than the
aforementioned countries, so per capita terrorism is still unusually high for a developed country.
As such, although the nature of terrorism in the West has changed, application of our results to
other countries should be taken with a grain of salt.
shows a decline in the amount of investment.
Other contributions highlight the impact of terrorism on GDP and tourism
(Ruiz Estrada and Koutronas, 2016), ination (Shahbaz, 2013) and the exchange
rate (Gerlach and Yook, 2016). Local …rms’behavior changes with respect to the
local environment, leading to changes, for instance, in the in‡ation of nondurable
goods prices in order to sell perishable stocks. Change in in‡ation is associated
with foreign investment reallocation leading to changes in the exchange rate.
In our paper, we focus on the impact on ination and exchange rate forecasts
using both expert and market expectations (implied by the price of in‡ation-
indexed bonds and the forward exchange rate) in Israel. We restrict ourselves
to these indicators due to a data availability constraint, since these are the only
indicators for which both market-implied and expert forecasts are available at a
monthly frequency over a su¢ ciently long period to provide meaningful estimates.5
Before conducting a predictive ability analysis, we will lay the foundation by
performing a dynamic analysis of rationality and (relative) forecast performance.
Our assessment relies on the tests for (relative) forecast performance and forecast
rationality proposed by Giacomini and Rossi (2010) and Rossi and Sekhposyan
(2016), respectively, for unstable environments. While neither of these tests allow
for a formal assessment of our key hypothesis that terrorism a¤ects forecast per-
formance, they are extremely helpful because (a) they allow a visual inspection of
forecast performance embedded into a structured framework, and (b) they allow
a broader look at performance that accounts for both relative errors and encom-
passing. This will allow us to show the relation between terrorism and forecast
performance on a more intuitive level.
Based on preliminary evidence gathered by the results of these tests and Gia-
comini and White (2006), we conduct an explicit analysis of the causes of forecast
performance. We control for a range of other aspects of uncertainty and instability
to ensure our results are not driven by an omitted variable bias. Speci…cally, we
control for …nancial instability, commodity prices (particularly oil and gas), ex-
change rate ‡uctuations, and an econometric forecast of in‡ation (exchange rate)
uncertainty. Because the conditional relative performance test by Giacomini and
White (2006) does not allow for control variables, we propose a slight modi…cation,
turning the original correlation-based Wald test into a regression-based Wald test.
To the best of our knowledge, this study is the …rst to conduct a broad analysis
of how terrorism a¤ects forecast performance and, particularly, the …rst to compare
several types of forecasts through derent terrorism measures re‡ecting the media
coverage, nationality and geographic dimensions of the attacks. We …nd that
terrorism a¤ects market participants much more than professional forecasters. At
least in the case of Israel, the low average performance of market participants
seems to be driven mostly by terrorism. In addition, we …nd that terrorist attacks
ect forecasting performance controlling for the risk premium.
Recent studies about macroeconomic uncertainty, including Farhi and Gabaix
(2016) and Scotti (2016) do not deal with terrorism or short-term warfare. Our
paper is novel in this respect. Although our key research question is in regard to the
5Which is not the case of other economic variables such as consumption or investment.
impact of terrorism on expectations, we also contribute to the growing literature
comparing market-implied forecasts and professional forecasts in general (Adeney
et al., 2017). Contrary to most of the literature, we do so while fully accounting
for the dynamics of forecast performance, linking us to the literature on forecast
performance in unstable environments. Most notably this literature includes the
papers by Giacomini and White (2006), Giacomini and Rossi (2010), and Rossi
and Sekhposyan (2016), whose methods we borrow. However, the literature has
grown far beyond those original papers, including an abundance of applications
such as Barnett et al. (2014) and El-Shagi et al. (2016), to name just a few.
The remainder of the paper is organized as follows. Section 2 describes the
stylized facts and related economic forecasts that are analyzed. Section 3 develops
our methodology and the econometric techniques used to quantitatively assess
the impact of terrorist attacks on economic forecasts. The results are presented
in Section 4 and interpreted in Section 5. In Section 6, we outline some policy
implications of our …ndings. Section 7 concludes.
2 Background
Although there is reason to believe that terrorism might a¤ect agents’psychology
and expectations, it seems that the literature and institutions making regular
use of forecasts ignore this channel. While many previous studies analyze the
impact of terrorism on current economic activity, few, if any, analyze the impact of
terrorism on these essential economic forecasts. Unfortunately, Israel is a relevant
laboratory in which to study this impact. Section 2.1 presents some stylized facts
about terrorism, Section 2.2 describes the market-based and expert forecasts used
in this study, and Section 2.3 details further control variables used for the analysis.
Our analysis spans from 2000 to 2017, using CPI in‡ation data at a monthly
frequency and terrorism and exchange rate data at a daily frequency. The sources
and detailed transformations are presented below.
2.1 Terrorism and uncertainty
Major terrorist attacks (Keefer and Loayza, 2008; Roberts, 2009), or frequent small
and medium-sized terrorist attacks (Sandler and Enders, 2008; Benchimol, 2016),
could a¤ect the economy. The negative impact of terrorism on short-term activity
is a result of the reallocation of internal demand for public consumption (such as
insurance, security forces, and investments) to the detriment of more productive
investments, causing growth to decrease (Blomberg et al., 2004). Our purpose is
to assess how terrorist incidents a¤ect the bias and predictive abilities of experts
as well as market-based forecasts.6
In the aftermath of the September 11 attacks and with the rise of terrorist
attacks in the European Union, there has been increasing interest in the economic
6We conducted di¤erent event studies without robust results. Daily variance in market-based
and expert forecasts cannot be explained using terrorism. The e¤ect of terrorism or …nancial
uncertainty on expectations does not seem to be immediate but takes time to come about.
consequences of terrorism (Crain and Crain, 2006; Dorsett, 2013; Gerlach and
Yook, 2016; Ruiz Estrada and Koutronas, 2016) and the reasons behind it (Dreher
and Gassebner, 2008; Dreher and Fischer, 2010). In addition, linkages between
terrorism and economic policy have been extensively analyzed (Dreher et al., 2010;
Dreher and Fuchs, 2011).
In September 2001, when terrorists attacked the United States, the US econ-
omy was already in recession, but it reached positive growth only two months
later. This led to the conclusion that even a major terrorist attack, such as the
destruction of the World Trade Center, would have fairly limited economic con-
sequences. Similarly, after the terrorist attacks in Madrid (2004) and London
(2005), GDP growth trends in Spain and the United Kingdom were not a¤ected.
Even the attacks in Paris (2015) did not show a measurable impact on French
consumption. However, in almost all such cases, although the country is geo-
graphically or demographically large in regard to the consequences of terrorism,
expectations— including forecasts— were strongly a¤ected.
The emergence of a small, developed market economy in parallel with a wave
of terrorist attacks provides an interesting economic example (Eldor and Melnick,
2004; Caruso and Klor, 2012). Israel is a perfect case study of a (small) developed
country facing terrorism and war at di¤erent levels and frequencies (Eckstein and
Tsiddon, 2004; Larocque et al., 2010).
In the past two decades, there have been …ve episodes of intense violence
involving Israel: the Second Intifada (September 2000 to February 2005), the
Second Lebanon War (July–August 2006), Operation Cast Lead (December 2008
to January 2009), Operation Pillar of Defense (November 2012), and Operation
Protective Edge (July–August 2014).
Unlike most terrorist attacks occurring in Europe or the United States, terrorist
attacks in Israel (Fig. 1) have not involved substantial destruction of property
or infrastructure, except during the First and Second Intifada, but they have
sometimes led to substantial casualties (Fig. 2).
Nevertheless, terrorist attacks a¤ect consumer and investor behavior and, in
turn, stock market prices (Shoham et al., 2011; Kollias et al., 2011). When ter-
rorists strike at regular intervals and fear and insecurity win minds and begin to
change agents’economic behavior, the quality of economic (expert and market-
based) forecasts would be a¤ected by these transitory events.7The psychological
ects on expectations and feelings of uncertainty might be considerable, giving
our study the unique ability to assess the impact of uncertainty, rather than the
unforeseen e¤ects of negative shocks (Romanov et al., 2012).
In this study, we use three sources of statistics on terrorist attacks to mea-
sure terrorism in Israel, each including four di¤erent indicators: number of people
killed during terrorist attacks, number of people wounded during terrorist attacks,
total number of casualties (killed and wounded) during terrorist attacks, and total
number of terrorist attacks. We use one academic source (Global Terrorism Data-
7Indeed, forecasters expect the cost of security policies to increase, thus increasing the ex-
pected cost of economic activities and transactions, while large companies are expected to cancel
or delay investments.
Figure 1: Number of terrorist attacks in Israel between 2000 and 2016. Source:
National Consortium for the Study of Terrorism and Responses to Terrorism
(START), Global Terrorism Database.
base,8hereinafter GTD) and two government sources (Ministry of Foreign A¤airs,
MFA; and the National Insurance Institute,9NII). The number of terrorist attacks
is not available in the MFA data.
These sources have their own methodology to account for terrorist attacks and
casualties. For robustness purposes, and to capture the di¤erent dimensions and
psychological components underlying each terrorism measurement methodology,
fear instilled by the media (GTD), fear a¤ecting a speci…c nationality (NII), or
geography (MFA), we use these di¤erent data sources.10
GTD data are generated by isolating an initial pool of potentially relevant arti-
cles and then using sophisticated natural language processing (NLP) and machine
learning techniques to further re…ne the results, remove duplicate articles, and
identify possibly relevant articles. The GTD team manually reviews this second
subset of articles to identify unique events that satisfy the GTD inclusion criteria,
and then studies and codes them according to GTD speci…cations. GTD data
are reported separately for pre-1967 Israel, and Gaza and West Bank. To achieve
8Database supported by the University of Maryland and maintained by the National Con-
sortium for the Study of Terrorism and Responses to Terrorism (START).
9Which provides social security services in Israel, among other things.
10 Terrorism data are also collected by the Israel Defense Forces and B’tselem. However, those
data are not su¢ ciently recognized in the academic literature, and are potentially less objective
than the three databases we consider.
Figure 2: Number of killed and wounded during terrorist attacks in Israel between
2000 and 2016. Source: National Consortium for the Study of Terrorism and
Responses to Terrorism (START), Global Terrorism Database.
better comparability, in particular with the MFA data, and to account for the fact
that most attacks in Gaza and West Bank target Israelis, we aggregate the two
datasets. However, tests conducted with only terrorism data on pre-1967 Israel
lead to similar results.
MFA data are from the chronology of terrorist attacks in Israel published by
the Israeli Ministry of Foreign A¤airs and collected by Johnston (2016). MFA
data include West Bank and Gaza in their denition of Israel.
NII data are from the National Insurance Institute (the State’s social security
agency). These data include terrorist attacks involving Israelis all over the world,
without geographical distinction. That is, in contrast with our other sources (GTD
and MFA), this database contains terrorist attacks outside Israel as well.
The most precise database is NII, as the records are double checked–government
and social security services–and not based on text mining (GTD) or geography
only (MFA). However, we believe GTD has the crucial advantage, due to its re-
liance on media coverage, of being related more closely to terrorism "perceptions",
which is essential for the psychological transmission channel to work.
Although during periods of frequent terrorist attacks, there were months with
very few or no terrorist casualties or attacks (e.g., there were roughly 10 peo-
ple injured in February 2003 and in July 2003, at a time when …gures in most
months were in the hundreds), we use a backward-looking 12-month moving aver-
age for all terrorism indicators because …nancial data and turmoil are inherently
persistent while the e¤ects of terrorism could last a long time (Marsden, 2012;
Bandyopadhyay et al., 2014; Karl et al., 2017). The moving average accounts for
high volatility by correctly identifying the respective months as part of periods
with high instability. Regressions with 3- and 6-month moving averages lead to
weaker but qualitatively similar results.
2.2 Market and expert forecasts
Israel is one of the rare developed countries with sound economic institutions and
developed …nancial markets (and thus access to very detailed economic informa-
tion) to have experienced a long history of frequent terrorism. We are particularly
interested in market expectations and professional forecasts. Since Israel issues
both in‡ation-indexed and unindexed bonds, it is straightforward to compute mar-
ket expectations of ination (breakeven in‡ation rates).
As a measure of professional forecasts, we use the combined professional fore-
cast assembled by the Bank of Israel from di¤erent professional sources.11
Expert as well as market-based in‡ation and exchange rate forecasts are useful
when formulating the in‡ation-targeting monetary policy of a small open economy.
Thus, the Bank of Israel collects a set of forecasts that are updated regularly.12
As per the Bank of Israel’s practice, we use the forecasts as given without
further risk adjustment. Although this implies that our measures are not perfect
measures of expectations, it guarantees that they are perfect measures of poli-
cymakers’perception of expectations, which is more important. However, we do
control for in‡ation risk and exchange rate risk to ensure that our results are not
driven by lack of risk adjustment.13
In this study, we use two types of market-based 1-year (1Y) Consumer Price
Index (CPI) in‡ation forecasts used by the Bank of Israel’s Monetary Policy Com-
mittee (MPC): the 1Y forward (contract) implied in‡ation forecast and the 1Y
breakeven (zero-coupon bond implied) in‡ation forecast. The …rst is the instanta-
neous 1Y forward in‡ation rate, and the second is reported by the Bank of Israel
as the o¢ cial 1Y market-based ination forecast.14 These time series are not
transformed and are used as is by the MPC.
In addition, the Bank of Israel collects a series of forecasts provided by pro-
fessional forecasters, giving an overview of the professional in‡ation expectations.
11 The Bank of Israel publishes an average of forecasts provided by several …nancial institutions.
There are roughly 11 providers of in‡ation forecasts and 6 providers of exchange rate forecasts
(on average, mainly commercial banks) over our sample.
12 See, for example, publicly available minutes related to interest rate policy decisions. The …rst
section, related to in‡ation, as well as almost all sta¤ forecasts, mention expert and market-based
13 See below and Section 4.1 for more details about the in‡ation risk premium.
14 This measure is assumed to deal with several inherent breakeven in‡ation problems. It
considers the small number of real bond series, bias derived from the indexation mechanism
(indexation lags and other mechanisms impacting the calculation of the yield to maturity of the
CPI-indexed bonds), and CPI seasonality a¤ecting the pricing of CPI-indexed bonds. However,
in‡ation risk premiums as well as bias derived from di¤erences in taxation and liquidity between
di¤erent bond types are not considered.
Contrary to many other surveys, individual forecasters are not asked for their
opinions at a given point in time, but they are able to update their forecasts at
will, thereby giving us daily data on professional forecasts.15 The expert forecasts
used in our study are computed as the simple arithmetic mean of the ination
forecasts of commercial banks and economic consulting …rms. This measure to-
gether with the 1Y breakeven in‡ation forecast are reported in o¢ cial publications
of the Bank of Israel.16
The situation is equally good for exchange rates. There is an active ILS/USD
future market that allows us to infer market expectations for the exchange rate.
Given their importance, exchange rates are covered by the Bank of Israel’s in-house
survey of expert (banks) forecasters.17
In this study, we use the 1Y forward (contract) implied USD/ILS exchange rate
forecast as our market-based exchange rate forecast. As practiced in the Bank of
Israel the implied exchange rate is not transformed or adjusted in any further way.
The forecasts are obtained for the last day of the month. Variables used to
explain forecast performance are from the month when the forecast is made. Thus,
they can a¤ect the forecast, and do not just appear as a forecast error by occurring
after the forecast is made. One year is the only forecast horizon where market and
expert forecasts match in Israel.
Detailed descriptions of the performance of the in‡ation and exchange rate
forecasts are provided in Sections 4.1 and 4.2, respectively.
2.3 Further control variables
Since we are particularly interested in conditional forecast performance, it is im-
portant for us to guarantee that a signi…cant conditionality of forecast performance
on terrorism is not due to omitted variable bias. There are other variables that
are strongly linked to uncertainty that might be correlated with terrorism. There-
fore, our analysis considers a range of control variables explained in the following
Most importantly, market expectations and professional forecasters respond
15 Strictly speaking, expert forecasts are not at a daily frequency. The Bank of Israel collects its
own expert forecasts based on a system allowing non-costly and private updating ability of their
forecasts in an unlimited way. If the forecast is not updated, the previous value is considered.
However, because we consider an average of these expert forecasts, and almost all experts update
their forecast every week on average over the reviewed period, this time series frequently changes
during the month.
16 Every month, the Bank of Israel publishes a press release. Its section on monetary policy and
in‡ation (data and reports) details the expected rate of in‡ation derived from various sources.
17 The …nal variable for which implicit market forecasts exist is interest rates, whose expecta-
tions can be computed from the yield curve. However, the expert forecasters cover the policy rate
by the Bank of Israel. Nonetheless, the expectation on interest rates from Israeli treasury bills,
implied by the term structure, constitutes an implicit forecast for the treasury bill rate. While
close to each other, the two interest rates (policy rate and treasury bill rate) are not precisely
the same. In addition, the nominal interest rate did not change signi…cantly since 2014, while
other indicators (terrorism and control variables detailed in the next section) changed drastically.
Therefore, we exclude the nominal interest rate from our study.
to …nancial uncertainty.18 It is well established that asset prices can predict in-
ation (Stock and Watson, 2003) and foreshadow tail risks in in‡ation (de Haan
and van den End, 2018), and stock, bond and foreign exchange market commove
(Pavlova and Rigobon, 2007). Correspondingly, …nancial market uncertainty can
drive in‡ation uncertainty.
Additionally, our …nancial control variables allow us, to some extent, to account
for the possibility that the impact of terrorism on forecasts is indeed driven by its
impact on …nancial markets. For example, terrorism can a¤ect market liquidity
depending on the size of the incident (Chen and Siems, 2004). However, rare
large-scale terrorist attacks, combined with improvements in market resilience as
well as …nancial stability during the last decade, make liquidity issues less likely
to a¤ect our analysis (Peleg et al., 2011).
Financial variables have the additional advantage of usually exhibiting con-
siderable comovement with the business cycle. This is crucial since we cannot
explicitly control for business cycle indicators –which are typically measured at
quarterly intervals –due to our monthly frequency.19
Then, we employ three derent measures of volatility to serve as control vari-
ables. First, we use the monthly standard deviation of daily returns (approximated
as log di¤erences) of the relevant stock market index, that is, the TA-100 index,
which is the broadest leading index in the Tel Aviv Stock Exchange.20 Second,
we include the spread between the highest and lowest levels of this index within
the month of the forecast. Third, we consider the monthly average of the corre-
sponding daily spreads. While the monthly spread reacts more strongly to major
movements within a month, the average daily spread implicitly gives higher weight
to intraday ‡uctuations.
The …nancial uncertainty measured as the TA-100s one-month rolling win-
dow volatility (standard deviation) and daily spread (high-low spread) between
2000 and 2016 displays a Pearson correlation of 0.49. The highest level of …nan-
cial uncertainty was reached during and around the collapse of Lehman Brothers
(2008Q3–Q4) and the European and Greek debt crises (2010Q1, 2011Q2–Q3, and
2015Q3). Volatility was also high during most of the Second Intifada period and
the (unanticipated) elections for the 18th Knesset21 held in 2009Q1.
In addition, since terrorism in Israel might be related to unrest in the Middle
East as a whole, which has serious repercussions for the price of oil, a major
factor in global economic development, we control for commodity price volatility.
More precisely, in our empirical analysis, we control for the monthly volatility
18 The global …nancial crisis and subsequent recovery period provided new insights about fore-
cast evaluation during the period of data instability in both the euro area (Benchimol and
Fourçans, 2017) and the United States (Caraiani, 2016), as well as in Israel (Benchimol, 2016).
19 Even unemployment, which is traditionally a monthly business cycle indicator, cannot be
considered for Israel. Unemployment …gures have only been produced at a monthly frequency
since 2012, having been produced on a quarterly basis before that.
20 The TA-100 index consists of 100 shares with the highest market capitalization, and includes
the TA-25 and TA-75 indices.
21 The Knesset is the unicameral national legislature of Israel.
(standard deviation of daily log di¤erences) of crude oil,22 natural gas,23 and CRB
commodity price index24 expressed in ILS and USD.
Finally, we control for the volatility of the USD/ILS exchange rate, again
computed as the standard deviation of daily log di¤erences over one month.
3 Methodology
While early literature on forecast evaluation usually evaluated forecast perfor-
mance for the entire sample, the past decade has seen the emergence of literature
on forecast evaluation in unstable environments, which accounts for situations
such as those described in Section 2. These new tests allow us to assess time
variation in both the performance of individual forecasts and relative performance
of forecasts, as well as to account for the fact that some models and/or forecasters
might do well in some situations, but not in others.
Some tests involved are essentially supremum versions of established tests over
a rolling window of forecasts. This introduces a multiple testing problem that
causes the critical values of those tests to be much higher than those of the under-
lying individual tests. If there is no ‡uctuation, this causes an unnecessary loss in
power. Thus, the tests are often accompanied by full sample versions. However,
since most of our results indicate strong rejection, we omit reporting these full
sample tests in this paper.
3.1 Rationality in unstable environments
We start the analysis with the most fundamental question, that is, whether the
forecasts we consider are rational. Rossi and Sekhposyan (2016) suggested using
the maximum of rolling-window Wald-type rationality tests as the test statistic.
Thus, the null hypothesis is that the forecast under consideration is rational at
every point in time during the sample. Rejection does not imply that a forecast is
permanently irrational, but that it was irrational at least once during the sample
22 West Texas Intermediate (WTI) crude oil spot price, US dollars per barrel, not seasonally
adjusted. Because of its sulfur concentration, WTI is a better grade of crude oil for the production
of gasoline, while Brent oil is more favorable to the production of diesel fuels. Brent’s higher
sulfur content a¤ected its price as it was more expensive to re…ne into gasoline. The series of anti-
government protests, uprisings, and armed rebellions known as the Arab Spring began spreading
across the Middle East in December 2010, and the civil war in Libya escalated in February 2011.
These events created concern and worries about the instability in the region, causing volatility
in the oil market. The price of the Brent crude rallied relative to WTI crude, the reason being
concerns over the availability of supply and logistical considerations, e.g., possible violence on
seaway passages. Other world events a¤ected Brent pricing, such as the agreement that Iran
would increase the daily amount of Iranian crude ‡owing into the market. Since Brent is the
pricing benchmark for Iranian crude, this pushed the price of Brent down relative to WTI. Last
but not least, recent literature uses WTI instead of Brent for Israel (Choi et al., 2018).
23 Henry Hub natural gas spot price, US dollars per million British Thermal Units (BTU), not
seasonally adjusted.
24 Thomson Reuters/CoreCommodity Commodity Research Bureau (CRB) index.
period. Following the original research, we report the entire time series of under-
lying individual test statistics to obtain a visual representation of which periods
cause the rejection. We apply the same strategy for other related tests.
The underlying test statistic is based on a standard Mincer and Zarnowitz
(1969) regression:
yt+h=+^yt+h;t +t;h;(1)
where yt+his the variable of interest at t+h,^yt+h;t is the corresponding forecast
made at time t, and t;h is the error term of the test regression. The traditional
rationality test examines the joint hypothesis that = 0 and = 1. It is straight-
forward to observe (as pointed out, for example, by West and McCracken (1998))
that this can be rearranged so that the h-step-ahead forecast error at time t,
^t;h =yt+h^yt+h;t, becomes the left-side variable:
^t;h =0+1^yt+h;t +t;h;(2)
where 0and 1are the regression coe¢ cients of the adjusted test equation.25
This gives us the more approachable null hypothesis =0
1=0, which
can easily be assessed with a standard Wald test using the test statistic:
where ^
is the estimator of and ^
is the corresponding heteroscedasticity and
autocorrelation consistent (HAC) robust estimator of the covariance matrix of ^
With a sample of Pforecasts and using window length m, the proposed test
statistic takes the following form:27
j2fm;:::;P g
where Wj;m is a Wald statistic (as de…ned in Eq. 3) computed on a subsample
using observations jm+ 1 to j.
The distribution of this test statistic depends on whether the uncertainty in the
parameter estimates in the forecasting model itself should be accounted for (i.e.,
not the uncertainty concerning but the degree of uncertainty in the parameters
used to generate ^y). In the case of expert or market-based forecasts, the so-called
model-free forecasts, the distribution under the null collapses to its most simple
form, depending asymptotically only on m=P and not the sample size used to
produce forecasts.
25 That is, 0=and 1=1.
26 Since it is well established that t;h follows an MA(h)process even for perfectly unbiased and
e¢ cient forecasts due to overlapping unforeseeable shocks over the hperiods of the forecast, an
HAC correction using a su¢ ciently high lag order is of utmost importance when not explicitly
modeling this moving average behavior. We follow the suggestion of Rossi and Sekhposyan
(2016) and use a standard Newey and West (1987) estimator for the covariance matrix.
27 In our study, we use a sample of forecasts of P= 177 and 25-month windows (m= 25)
in the kernel for our HAC variance estimators (12 months on both sides), which is above what
rules-of-thumb usually suggest. This should su¢ ce to correct the degree of MA introduced by
overlapping forecasts, taking into account the serial correlation.
However, using the asymptotic critical values can create fairly sizable distor-
tions in …nite samples. For typically available sample sizes (i.e., Pbetween 100
and 200) the number of observations for an individual window quickly becomes ex-
tremely small. We therefore use the …nite sample adjusted critical values provided
by El-Shagi (2019).
3.2 Stability of relative forecast performance
3.2.1 Fluctuation test
Much like the rationality test in unstable environments is a maximum of individ-
ual rationality test statistics over a rolling window, the test for relative forecast
performance in unstable environments proposed by Giacomini and Rossi (2010)
is the maximum of traditional relative forecast performance tests over a rolling
Similar to the previous test, the null hypothesis is that the forecasts under
consideration perform equally well at every point in time. Exceeding the critical
value does not imply that one model constantly outperforms the other, but merely
that there is a meaningful di¤erence in predictive ability for a subsample.
More precisely, the test statistic is the maximum of local Diebold and Mariano
(1995) test statistics, in which the variance estimator is based on the full sample
of forecasts, rather than the individual window for which the mean di¤erence in
predictive ability is computed. Since the full sample estimator is used in this
approach, mcan be much smaller than in the previously outlined test. This is
because the rationality test needs mto be su¢ ciently large to allow meaningful
estimation of , which corresponds to ^in this test, within the rolling window,
which is not relevant here. Denoting the loss function for the two forecasts under
consideration at time tby L1;t;h and L2;t;h and the corresponding loss di¤erence
by Lt;h =L1;t;h L2;t;h, we can write the test statistic as
j2fm;:::;P g^1m1=2
where ^is the HAC robust estimator of the standard error of the mean of Lt;h.
Since the …nite sample bias in the rationality test under instability is mostly
introduced by the uncertainty in the estimation of over mobservations, the …nite
sample problems are far less pronounced in this test, and we use the asymptotic
critical values provided by Giacomini and Rossi (2010).
The test we use is two sided, because there is no valid prior assumption of the
superiority of one forecast over another. For the visual interpretation, we report
that ^1m1=2Pj
t=jm+1 Lt;h, rather than the corresponding absolute, which is
part of the test-statistic, to observe whether the rejection is driven by forecast 1
or forecast 2to be superior during a subsample.
Tests reported use squared forecast errors as a loss function. Performance
di¤erences are de…ned as market-based loss less expert-based (survey of bank’s
forecasts) loss. Thus, high values of the test statistic indicate worse performance
of market-based forecasts, and corresponding superiority of the expert forecasts.
3.2.2 One-time reversals in forecast performance
Often, a potential change in forecast performance is due to a single structural
break (e.g., introduction of a new forecasting model or policy that is not well
understood by one forecasting agent), rather than ‡uctuations over time. In this
case, the very ‡exible framework outlined above still creates an unnecessary loss
in power, compared with a test that explicitly models a single structural break.
Thus, Giacomini and Rossi (2010) proposed a so called one-time reversal test,
which follows the spirit of the supremum structural break tests introduced by
Hawkins (1987).
Technically, the test includes a testing procedure composed of three separate
The …rst test statistic is a straightforward full sample test:
LM1= ^2P1"P
The second is the actual structural break statistic based on the loss derences
in various subsamples:
LM2= max
LM2(j) = ^2P1(j=P )1(1 j =P )1"j
Lt;h (j=P )
Finally, the joint test-statistic with the null hypothesis of equal performance
at any point in time is as follows:
Correspondingly, if the third test statistic is rejected, we can reject equal per-
formance at every point in time. Only then do we assess the individual underlying
statistics LM1and LM2. If only LM1is rejected, this indicates the permanent su-
periority of one model. If only LM2is rejected, this indicates the reverse, in which
one model is superior only for a certain subsample. If both tests are rejected,
then the interpretation is not as clear cut, but it generally implies some change
in relative performance that is not strong enough to a¤ect the relative order of
forecasts. If there is evidence of a structural break, the most likely breakpoint is
j= argmax
3.3 Encompassing in unstable environments
Even if one forecast is permanently or temporarily better, this does not necessarily
imply that the superior forecast fully exploits the available information at all
times. Thus, we move to the next step and test forecast encompassing in the same
framework, proposed by Rossi and Sekhposyan (2016), which we used to assess
The key di¤erence is that the equation underlying the Wald test changes to a
standard encompassing equation given by
^1;t;h =0+1(^1;t;h ^2;t;h ) + t;h;(10)
where ^1;t;h and ^2;t;h are the forecast errors of models 1and 2, respectively.
Contrary to the rationality test, we are merely interested in 1. Thus, the
individual Wald statistics collapse to
where ^!is the lower right element of ^
and ^
1is the estimator of 1.
3.4 Conditional relative performance
After exploring time variation in forecast performance and, more importantly,
relative performance, we now assess the reasons for the variation. To that end, we
employ the test for conditional forecast performance proposed by Giacomini and
White (2006).
Denoting the set of conditions that potentially explain the di¤erence in per-
formance at time tby row vector ht, the test statistic is given by
T=P P1
1 P1
where Tis a standard Wald statistic based on pairwise correlations between the
elements of htand Lt;h.^
is the corresponding HAC robust estimator of the
covariance matrix. The test statistic follows a simple 2
qdistribution, in which q
is the number of elements in ht.
The null hypothesis is that forecast performance is not related to any indicator
collected in ht. The explanatory variables are usually, and in our example, mea-
sured at time trather than at t+h; that is, we do not assess what kind of shock
at t+his unforeseeable for certain forecasters, but we assess conditions at twhen
the forecast is made. To a certain degree, this allows us to choose the preferred
forecast, ex ante, that is, when the forecast is made, rather than later when the
realization is known.
It must be noted that including several indicators in htdoes not “controlfor
indicators in the sense of regression analysis, since the elements of htare simple
pairwise correlations rather than regression coe¢ cients. However, we also want to
assess whether terrorism truly has an impact that is not related to …nancial market
uncertainty. Nonetheless, due to the aforementioned construction, performing a
Wald test on only one coe¢ cient estimated jointly with others merely yields a
result that is obtained when testing this individual explanatory variable without
controlling for further indicators.
Therefore, we also run an ad hoc variation of this test, in which we use regres-
sion coe¢ cients rather than correlation coe¢ cients and the corresponding covari-
ance matrix, and we run a Wald test on the coe¢ cient(s) of interest only. The
underlying regression is
Lt;h =0+1terrort+2controlt+t;h;(13)
where terrortcan be any of our terrorism indicators discussed in Subsection 2.1
and controltany of the covariates introduced in 2.3.
4 Results
This section presents the results for tests related to the CPI in‡ation and the
USD/ILS exchange rate, as well as expert and market-based forecasts.
Our regressions are monthly mainly because we chose the forecasts that best …t
reported in‡ation in terms of timing, and to avoid useless daily noise from …nancial
variables. This also makes sure that the forecasts can correctly be interpreted and
compared as one-year-ahead forecasts.
4.1 In‡ation
Rationality Our rationality test in unstable environments strongly rejects for
both expert forecasts and market-implied in‡ation expectations rationality (Fig.
Since the null hypothesis of the underlying test is that the forecast is always
rational, this does not imply irrationality on average or even for a majority of the
periods. A visual inspection of the time series of the individual Wald statistics
that underlie the (supremum) test statistic used indicates that, in both cases, the
rejection is primarily driven by a strong bias following 2012Q3, which is mainly
related to warfare instability (Operation Pillar of Defense, 2012Q4, and Operation
Protective Edge, 2014Q3). Furthermore, the underlying Wald statistics pick up
some movement during the highest violence level of the Second Intifada, Operation
Cast Lead (2008Q42009Q1), unanticipated Israeli legislative elections (2009Q1)
and …nancial uncertainty until 2010Q3.
Even this preliminary evidence, based on rationality tests, lends quite strong
support to our main hypothesis; that is, uncertainty and instability of any form,
whether …nancial or— as in the unfortunate case of Israel— caused by terrorism,
strongly a¤ects expectations and thereby forecasts.
Relative forecast performance and encompassing Both expert and market-
based in‡ation forecasts are strongly a¤ected by uncertainty, and the order of
magnitude seems similar. Although a ‡uctuation test rejects the null hypothesis
of equal forecast performance at each point in time, this rejection is exclusively
driven by the early period of the sample, when experts hugely outperformed mar-
ket expectations. Fig. 4 shows development of the underlying individual test
(a) Expert forecast (b) Market-based forecast
Figure 3: Rationality test statistic (solid) and the critical value (dashed) for CPI
in‡ation. Note: Signi…cance level: 5%. Short warfare (dark gray): Second Intifada (A), Second
Lebanon war (B), Operation Cast Lead (C), Operation Pillar of Defense (D), Operation Pro-
tective Edge (E). Relatively long terrorism period (light gray): rockets from Gaza and Lebanon
(T1), mortars and rockets from Gaza, Flotilla episode and terrorist attacks (T2), terrorist at-
tacks (including abroad), mortar and rockets from Gaza (T3), and Stabbing Intifada mixed with
periods of rockets and mortars from Gaza (T4).
statistics over time, indicating another extended period of superior expert fore-
casts in the second half of the 2000s. However, the test statistics are far below
the critical value. At other times, the performance of both forecasts is almost
These prolonged but insigni…cant ‡uctuations in relative performance cor-
respond to periods of fear (Second Lebanon War and victory of Hamas in the
Palestinian legislative elections of 2006, as well as Palestinian Gaza-based mor-
tar attacks and Israeli missile strikes during 2011–2013). The magnitude of the
uctuations is small enough that we cannot rule out a single structural break in
relative forecast performance around 2004, as indicated by the one-time reversal
The encompassing test (Fig. 5) paints a clearer picture.
Again, we clearly reject that the typically superior expert forecasts encompass
market forecasts. These rejections are mainly driven by the very same periods
that drive the results of our rationality test. That is, while both experts and
market participants are strongly a¤ected by uncertainty, they are a¤ected in very
di¤erent ways, which leads to the natural follow-up question: Can uncertainty
indeed explain the di¤erences in forecast performance?
Conditional relative forecast performance Our core question is whether
terrorist attacks a¤ect the (relative) performance of expert and market-based fore-
(a) Fluctuation test (b) One-time reversal test
Figure 4: Fluctuation and one-time reversal test statistics (solid), and the critical
value and average loss di¤erences (dashed) in (a) and (b), respectively, for CPI
in‡ation. Note: Signi…cance level: 5%. See Fig. 3 for an explanation of the labels.
casts. All indicators of terrorism we use turn out to be signicant at the 5% level.28
To assess the robustness of these results, beyond the inclusion of a range of
di¤erent indicators, we test speci…cations controlling for the alternative reasons
for time-varying forecast performance that might be correlated with terrorism.29
First, we include several indicators of …nancial uncertainty (Section 2.3). Terror-
ism might cause market turbulence that in turn a¤ects economic forecasts, just
as …nancial turmoil might do when caused by any other source. Second, we in-
clude commodity price volatility as well as USD/ILS volatility. Since terrorism in
Israel might be related to instability in the Middle East, where several main oil
producers are located, there might be a relationship between in‡ation forecasts
and commodity price volatility. Commodity prices are known to have a signi…cant
in‡uence on the world economy and, specically, on Israel, which is a small open
economy, importing oil, and was a gas importer before developing its natural gas
Tables 1 and 2 present detailed results of conditional predictive ability tests
28 In addition to the standard model, we run an extended model in which we account for
potential nonlinearity by including the square of the terrorism term. In cases in which we …nd
a signi…cant square term, it is combined with a highly signi…cant coe¢ cient on the level. That
is, there is some evidence that the impact of terrorism on forecast performance declines quickly.
In other words, it is the existence of terrorism, rather than its degree, that a¤ects forecast
performance. However, these results are far from robust and strongly depend on the selection
of the terrorism indicator. Therefore, we opt to exclude said nonlinearity from our baseline
speci…cation. More detailed results are available upon request.
29 As a robustness check, we also conducted this exercise with terrorism data excluding ter-
rorism in Gaza and West Bank, thus using only GTD measures (data on Israel without Gaza
and West Bank). Our results lead to similar conclusions as presented below. These results are
available upon request.
Figure 5: Encompassing test statistic (solid) and the critical value (dashed) for
CPI in‡ation. Note: Signi…cance level: 5%. See Fig. 3 for an explanation of the labels.
(CPA) when considering the joint test of both market and expert in‡ation forecasts
against control variables. In our analysis we focus on signi…cance. To maintain
information on the direction of the e¤ect, our tables report t-statistics rather
than p-values. Terrorism has a signi…cant e¤ect on relative forecast performance
independent of the control variables included in the model. In all cases, the impact
of terrorism on the loss di¤erence is positive, implying that the quality of market
forecasts deteriorates more strongly in times of high terrorism than that of expert
Our modi…ed test relies on regression coe¢ cients rather than correlations for
the underlying Wald test statistics, and still …nds indicators for terrorism signi…-
cant after including …nancial uncertainty, USD/ILS, or commodity price changes
as control variables. The only exception is the number of attacks, as measured by
GTD, since in most cases, we …nd signi…cance for neither the number of attacks
nor the additional control, but we do …nd individual signi…cance for the number
of attacks, without additional controls, and joint signi…cance. We achieve the best
results when using NII indicators, particularly— and surprisingly— the number of
attacks as measured by NII.
Most alternative indicators of uncertainty, that is, …nancial indicators and com-
modity prices, seem to play a role when assessed individually, but they are not
robust. While we …nd that most indicators still seem signi…cant for some terror-
ism indicators, those results are not stable and strongly depend on the terrorism
indicator chosen.
The only exception is gas price volatility, which remains signi…cant in all re-
gressions. However, in all those cases, the corresponding terrorism indicator is
still signicant, again with the exception of the number of attacks, as reported
by GTD. Based on the overwhelming robustness of our …ndings for 10 out of 11
terrorism indicators, we …nd fairly strong evidence that terrorism has an e¤ect on
Terrorism Control variable CPA terror cont rol CPA terro r co ntrol C PA terro r control
Killed 0.02 0.01 0.02
TA-100 vol. 0.03 1.34 2.47 0.03 1.92 2.34 0.03 1.32 2.46
TA-100 spread 0.03 2.87 0.85 0.02 3.33 1.07 0.03 2.94 1.03
USD/ILS vol. 0.03 2.23 0.82 0.03 2.93 0.95 0.04 2.47 0.96
Oil* vol. 0.03 1.41 2.51 0.03 1.75 2.29 0.04 1.14 2.43
Gas* vol. 0.04 1.93 2.12 0.03 2.57 1.82 0.04 1.70 2.05
CRB* vol. 0.03 3.02 1.64 0.03 3.21 1.60 0.03 2.80 1.63
Oil vol. 0.03 1.54 2.22 0.03 2.32 2.14 0.04 1.48 2.21
Gas vol. 0.04 1.93 2.14 0.03 2.58 1.86 0.04 1.71 2.08
CRB vol. 0.03 2.52 1.20 0.03 2.98 1.26 0.03 2.61 1.32
Wounded 0.01 0.01 0.01
TA-100 vol. 0.03 1.86 2.32 0.03 1.92 2.34 0.02 3.01 2.72
TA-100 spread 0.03 3.18 0.99 0.02 3.33 1.07 0.02 3.02 0.84
USD/ILS vol. 0.03 2.70 0.82 0.03 2.93 0.95 0.02 2.98 0.84
Oil* vol. 0.03 2.02 2.34 0.03 1.75 2.29 0.02 1.97 2.48
Gas* vol. 0.04 2.66 2.09 0.03 2.57 1.82 0.02 2.50 1.89
CRB* vol. 0.03 3.06 1.63 0.03 3.21 1.60 0.02 3.20 1.62
Oil vol. 0.03 2.35 2.11 0.03 2.32 2.14 0.02 2.38 2.26
Gas vol. 0.04 2.68 2.11 0.03 2.58 1.86 0.02 2.49 1.91
CRB vol. 0.03 2.83 1.20 0.03 2.98 1.26 0.02 2.89 1.14
Total 0.01 0.01 0.01
TA-100 vol. 0.03 1.78 2.35 0.03 1.81 2.37 0.02 2.90 2.66
TA-100 spread 0.03 3.21 0.97 0.03 3.29 1.07 0.02 3.24 0.91
USD/ILS vol. 0.03 2.68 0.82 0.03 2.88 0.95 0.02 3.13 0.86
Oil* vol. 0.03 1.93 2.38 0.03 1.64 2.32 0.02 1.97 2.43
Gas* vol. 0.04 2.61 2.10 0.03 2.45 1.87 0.03 2.62 1.89
CRB* vol. 0.03 3.10 1.64 0.03 3.18 1.61 0.02 3.39 1.64
Oil vol. 0.03 2.23 2.13 0.03 2.16 2.15 0.02 2.42 2.22
Gas vol. 0.04 2.63 2.12 0.03 2.46 1.90 0.03 2.61 1.91
CRB vol. 0.03 2.84 1.20 0.03 2.94 1.27 0.02 3.07 1.17
Number 0.02 0.01
TA-100 vol. 0.09 1.14 2.86 0.05 3.45 2.31
TA-100 spread 0.09 0.67 0.47 0.05 3.93 1.09
USD/ILS vol. 0.08 0.34 0.77 0.05 3.52 0.66
Oil* vol. 0.08 0.06 2.84 0.05 2.68 1.63
Gas* vol. 0.04 0.69 2.21 0.04 3.68 1.82
CRB* vol. 0.09 0.94 1.52 Legend 0.05 4.10 1.73
Oil vol. 0.07 -0.38 2.52 1% 0.05 2.76 1.50
Gas vol. 0.04 0.70 2.23 5% 0.04 3.67 1.80
CRB vol. 0.08 0.40 1.12 10% 0.05 3.51 0.96
Table 1: Predictive ability tests of 1-year breakeven and expert in‡ation forecasts.
Note: CP A is the p-value of the conditional predictive ability test when only a constant is
included. terror is the t-test of the terrorism variable considered. control is the t-test of the
control variable considered. A positive t-test means that expert forecasts are better than market-
based ones. A negative t-test means the opposite. T otal is the sum of those killed and wounded,
and Number is the quantity of terrorist attacks. Variables with are in USD.
Terrorism Control variable CPA terr or control CPA ter ror control C PA terro r co ntrol
Killed 0.05 0.06 0.05
TA-100 vol. 0.01 3.01 0.50 0.01 2.61 0.68 0.01 2.76 0.58
TA-100 spread 0.00 3.05 -0.37 0.00 2.69 -0.02 0.00 2.86 0.18
USD/ILS vol. 0.00 3.08 -1.30 0.00 2.62 -0.72 0.00 2.76 -0.64
Oil* vol. 0.04 2.99 0.25 0.04 2.59 0.21 0.04 2.75 0.15
Gas* vol. 0.08 3.03 2.26 0.08 2.56 2.10 0.08 2.74 2.07
CRB* vol. 0.01 3.01 -0.62 0.01 2.63 -0.74 0.01 2.77 -0.63
Oil vol. 0.03 2.97 0.40 0.03 2.59 0.46 0.03 2.75 0.46
Gas vol. 0.08 3.02 2.29 0.07 2.55 2.14 0.07 2.73 2.12
CRB vol. 0.00 3.05 -0.88 0.00 2.64 -0.45 0.00 2.78 -0.35
Wounded 0.04 0.04 0.01
TA-100 vol. 0.01 3.35 -0.16 0.01 3.08 0.20 0.02 2.62 1.40
TA-100 spread 0.00 3.40 -0.24 0.00 3.16 -0.11 0.00 2.59 -0.96
USD/ILS vol. 0.00 3.41 -1.49 0.00 3.10 -0.90 0.01 2.54 -1.35
Oil* vol. 0.04 3.30 -0.08 0.04 3.06 -0.09 0.03 2.54 0.29
Gas* vol. 0.08 3.40 2.20 0.08 3.06 1.80 0.05 2.47 1.91
CRB* vol. 0.01 3.34 -1.10 0.01 3.10 -1.19 0.01 2.56 -1.31
Oil vol. 0.03 3.26 0.12 0.03 3.05 0.32 0.02 2.56 0.55
Gas vol. 0.08 3.40 2.26 0.08 3.05 1.86 0.05 2.45 1.96
CRB vol. 0.00 3.38 -1.18 0.00 3.11 -0.89 0.01 2.58 -1.42
Total 0.04 0.04 0.01
TA-100 vol. 0.01 3.31 -0.03 0.01 3.04 0.27 0.02 2.77 1.31
TA-100 spread 0.00 3.36 -0.25 0.00 3.12 -0.05 0.00 2.75 -0.76
USD/ILS vol. 0.00 3.37 -1.45 0.00 3.05 -0.86 0.01 2.71 -1.26
Oil* vol. 0.04 3.27 -0.01 0.04 3.01 -0.05 0.03 2.69 0.22
Gas* vol. 0.08 3.36 2.22 0.08 3.01 1.85 0.06 2.67 1.90
CRB* vol. 0.01 3.31 -0.98 0.01 3.06 -1.09 0.01 2.72 -1.15
Oil vol. 0.03 3.24 0.18 0.03 3.01 0.34 0.03 2.71 0.50
Gas vol. 0.08 3.36 2.27 0.08 3.00 1.91 0.06 2.65 1.95
CRB vol. 0.00 3.34 -1.11 0.00 3.07 -0.80 0.01 2.74 -1.29
Number 0.00 0.01
TA-100 vol. 0.01 1.55 1.36 0.02 4.79 0.11
TA-100 spread 0.00 1.42 -1.55 0.00 4.88 -1.00
USD/ILS vol. 0.01 1.63 -1.92 0.01 5.40 -2.91
Oil* vol. 0.01 1.40 0.49 0.03 4.71 -1.12
Gas* vol. 0.01 1.50 2.24 0.04 4.91 1.86
CRB* vol. 0.01 1.36 -1.72 Legend 0.01 4.82 -1.63
Oil vol. 0.01 1.32 0.53 1% 0.03 4.70 -1.00
Gas vol. 0.01 1.52 2.28 5% 0.04 4.93 1.89
CRB vol. 0.01 1.52 -1.81 10% 0.01 5.14 -2.47
Table 2: Predictive ability tests of 1-year forward and expert in‡ation forecasts.
Note: CP A is the p-value of the conditional predictive ability test when only a constant is
included. terror is the t-test of the terrorism variable considered. control is the t-test of the
control variable considered. A positive t-test means that expert forecasts are better than market-
based ones. A negative t-test means the opposite. T otal is the sum of those killed and wounded,
and Number is the quantity of terrorist attacks. Variables with are in USD.
relative forecast performance.30
These detailed results highlight that forecasters are insigni…cantly better than
market-based forecasts for 1Y breakeven in‡ation, but signi…cantly better for 1Y
forward in‡ation.31
It seems plausible that the number of terrorist attacks as measured by NII is
the most powerful predictor of forecast accuracy. This result might be related to
the great importance forecasters and markets give to frequency compared with
severity of terrorist attacks (Pizam and Fleischer, 2002). Since the outcome of an
attack in terms of casualties is much more stochastic than its occurrence, it makes
perfect sense that people respond to the number of attacks, regardless of whether
the terrorists claimed many victims. Yet this seems to be contradicted by the
poor performance of the number of attacks as measured by GTD. This might be
driven by the screening methodology of GTD (text mining methodology), which
could cause minor attacks to be excluded, thereby creating a distorted measure of
the number of attacks while still being very accurate in terms of victims.
Similarly, the good performance of gas price volatility is hardly surprising,
given the role natural gas production has played for Israel in the last few years.
Interestingly, this indicates that uncertainty concerning external conditions (oil
and commodity prices) is far less important than factors that might actually a¤ect
production in Israel directly (e.g., gas prices and terrorism).
Forecast error vs. mismeasurement of expectations Technically, neither
of the market-based measures we use is a pure measure of in‡ation unless we
assume risk-neutral investors. Rather, breakeven in‡ation corresponds to the in-
ation expectation plus an ination risk premium. If the e¤ect of terrorism were
increasing uncertainty, for example, in terms of the political response,32 this might
easily create in‡ation risk. If agents were risk averse, this would a¤ect breakeven
in‡ation. Even if the accuracy of market expectations were to remain unchanged,
the forecast performance of breakeven in‡ation rates might thereby deteriorate,
as they are no longer an accurate measure of expectations.
In this section, we test whether this mismeasurement drives the deterioration
of the relative forecast performance of breakeven in‡ation from the sovereign bond
market compared with professional forecasters. To this end, we generate a measure
of in‡ation uncertainty that accounts for the potential impact of terrorism. We
estimate a recursive window, pseudo-out-of-sample, GARCH forecast of year-over-
year in‡ation. Both the variance of shocks and the process itself are modeled as
an autoregressive moving average model ARMA(1,1) process. We also include
terrorism as an exogenous determinant of shocks.33
30 Other results are available upon request. These tests were conducted with variables ex-
pressed in ILS and USD, and considered a 12-month moving average for the control variable.
All results con…rm that terrorism is the best explanatory variable of in‡ation forecast errors.
31 This result is mostly driven by the earliest part of our sample (Second Intifada).
32 See Berrebi and Klor (2008) for an analysis of the causal e¤ects of terrorism on the Israeli
electorate’s preferences.
33 We use the best-performing measure from our study, that is, the number of attacks as
reported by the NII.
Table 3 presents the results of a GARCH(1,1) model. The reported results
use the full sample. However, as mentioned above the time series for predicted
volatility are based on recursive-window out-of-sample predictions.
Model with terrorism Model without terrorism
Mean model
Coef. Std. Coef. Std.
Const. 0.859*** 0.281 0.867*** 0.288
AR (1) 0.644*** 0.061 0.648*** 0.061
MA (1) 0.952*** 0.013 0.952*** 0.013
Variance model
Const. 0.012 0.015 0.021* 0.011
AR (1) 0.160** 0.070 0.169** 0.067
MA (1) 0.768*** 0.092 0.749*** 0.090
terror 0.001 0.001
Table 3: GARCH model with and without terrorism. Note: Standard errors are robust.
***, **, and * indicate signi…cance at the 1%, 5%, and 10% level, respectively.
The available data on both terrorism and in‡ation go back much further than
our sample of professional forecasts. Therefore, our out-of-sample predictions for
the variance of in‡ation span our entire sample as used for the assessment of
forecast performance. In the full sample, we see that the impact of terrorism
on in‡ation variance is minimal at best, indicating that this is not the channel
through which terrorism a¤ects forecast performance.
A visual inspection of the time series of predicted standard deviation in‡ation
including and excluding terrorism (Fig. 6) con…rms this full sample result for the
recursive window estimation. Most of the time, there is no meaningful di¤erence
between the predictions generated by the two alternative models.
While we …nd that the standard deviation of expected ination as measured by
our GARCH has a robust and sizable impact on the relative forecast performance,
terrorism remains robust in explaining relative predictive ability.
In other words, our results are twofold. It seems that terrorism truly a¤ects
the predictive ability of market participants. Even when accounting for risk, we
nd that terrorism matters. Nevertheless, there is evidence that ination risk
matters. This indicates that market participants are far from risk neutral. This
makes using breakeven in‡ation rates as a substitute for forward-looking measures
of in‡ation in policymaking even more problematic.
4.2 Exchange rate
Rationality Regarding rationality, results for the USD/ILS exchange rate are
fairly similar to those for in‡ation. Fig. 7 shows that our rationality test in
unstable environments strongly rejects both expert forecasts and market-implied
exchange rate (USD/ILS) expectations.
Figure 6: Predicted standard deviation of in‡ation from the models with and
without terrorism.
The rejection is driven by a strong irrationality between 2007Q2 and 2008Q1
corresponding to the political and military con‡icts between Fatah and Hamas
leading to Hamas’takeover of Gaza (Battle of Gaza, 2007Q2–Q3). Substantial
terrorism and …nancial uncertainty (the Subprime crisis,34 2007Q2–2008Q4, and
Greek crisis,35 2009Q4–2010Q3) increased around these periods. As Section 4.1
shows, the underlying Wald statistics pick up some movement during the highest
violence level of the Second Intifada.
These rationality tests support the conclusion that terrorism strongly a¤ects
expectations and, thereby, expert and market-based forecasts.
Relative forecast performance and encompassing Like ination, expert
and market-based exchange rate forecasts are a¤ected by uncertainty of a similar
order of magnitude. Neither the ‡uctuation test (Fig. 8) nor the one-time reversal
test rejects, even at the beginning of the period (Second Intifada). This conrms
that at any point in time, expert and market-based forecast performances are
fairly similar (at the 5% signi…cance level).
34 In the the United States, Cecchetti (2009) and Mishkin (2010) consider that the crisis began
in 2007Q1, when several large subprime mortgage lenders started to report losses. The real
trigger for the crisis was in 2007Q3, when the French bank BNP Paribas temporarily suspended
redemptions from three of its fund holdings that had invested in assets backed by US subprime
mortgage debt. As a result, credit spreads began widening, interest rates in Europe shot up
overnight, and the European Central Bank immediately responded with the largest short-term
liquidity injection in its nine-year history (Benchimol and Fourçans, 2017).
35 At the end of 2009, the three main rating agencies downgraded Greece’s credit rating, and
during the …rst two quarters of 2010, three austerity packages were announced by the Greek
government. These decisions, also known as a cause of the European Debt Crisis, strongly
impacted the euro area, Israel’s main trade partner (Benchimol, 2016).
(a) Expert forecast (b) Market-based forecast
Figure 7: Rationality test statistic (solid) and the critical value (dashed) for
USD/ILS. Note: Signi…cance level: 5%. See Fig. 3 for an explanation of the labels.
Fig. 8 shows the dynamics of the underlying individual test statistics over
time. There is some indication that expert forecasts were superior at the begin-
ning of the sample, yet the test statistics are far below the critical value. Contrary
to in‡ation forecasts (Section 4.1), market-based forecasts were slightly superior
to expert forecasts between 2005Q1 and 2008Q4. This period corresponds to a
relatively calm period. The two transition points between superiority of market-
based and expert forecasts occurred at very speci…c times. The …rst corresponds
to strong fears involving the Hamas victory in the elections for the second Pales-
tinian Legislative Council, and the second corresponds to Operation Cast Lead
(2008Q4–2009Q1). While the …rst period heralded increasing fears in and around
Israel36 (and corresponded to the switch from the superiority of expert forecasts
to superiority of market-based forecasts), the second event promised a period
of increasing stability37 (and corresponded to the switch from the superiority of
market-based forecasts to superiority of expert forecasts). Operation Pillar of De-
fense (2012Q4) contributed to the performance deterioration of expert forecasts
relative to market-based forecasts.
The encompassing test (Fig. 9) highlights the clear rejection that the typically
superior expert forecasts encompass market forecasts. However, these rejections
are not driven by the same periods that drive the results of our rationality test.
Instead, we reject encompassing fairly consistently until 2008Q2. This indicates
that— for the exchange rate— markets constantly monitor and include factors that
36 The Battle of Gaza resulted in Hamas taking control of the Gaza Strip from Fatah (2007Q2);
the Israeli military’s launch of Operation Hot Winter (2008Q1); and regular, violent terrorist
attacks until Operation Cast Lead (2008Q4–2009Q1).
37 This relatively calm period lasted from 2009Q2 until Operation Returning Echo (2012Q1),
carried out to stop cross-border attacks, as mortars and rocket …re had started several quarters
(a) Fluctuation test (b) One-time reversal test
Figure 8: Fluctuation and one-time reversal test statistics (solid), and the crit-
ical value and average loss di¤erences (dashed) in (a) and (b), respectively, for
USD/ILS. Note: Signi…cance level: 5%. See Fig. 3 for an explanation of the labels.
are not properly accounted for by professionals. While this does not contradict
our hypothesis regarding the importance of terrorism, it does not provide s cient
support for it either.
Conditional relative forecast performance Interestingly, although the ini-
tial tests provide only weak evidence of ‡uctuations in relative forecast perfor-
mance, the tests for conditional forecast performance have di¤erent implications.
Generally, the results are not quite as clear as they were for ination. While
some conditional predictive ability tests reject some terrorism indicators at the
10% level, we fail to reject them in other cases.
The picture changes when accounting for other sources of uncertainty. While
the joint test now generally fails to reject the indicators because we add a less
powerful indicator, the individual t-tests for the terrorism indicators indicate con-
sistent rejections.
Table 4 presents detailed results of the CPA tests when considering the joint
test of both market and expert USD/ILS forecasts against control variables.
We …nd again that both terrorism and …nancial uncertainty explain variations
in the exchange rate’s (USD/ILS) relative forecast performance. In particular,
we …nd that market forecasts deteriorate more strongly in times of uncertainty
compared with expert forecasts. We …nd evidence that terrorism truly matters
for relative forecast performance, and the e¤ect of …nancial uncertainty becomes
insigni…cant when controlling for terrorism. Neither oil nor gas price ‡uctuations
play a stronger role than terrorism.38
38 As a robustness check, we also conduct this exercise with terrorism data excluding Gaza and
West Bank events (GTD). Our results are similar and available upon request.
Terrorism Control variable CPA terr or control CPA ter ror control C PA terro r co ntrol
Killed 0.09 0.11 0.12
TA-100 vol. 0.23 4.68 -0.29 0.25 3.46 -0.05 0.25 3.12 -0.04
TA-100 spread 0.23 4.62 0.44 0.26 3.66 0.80 0.27 3.26 0.67
USD/ILS vol. 0.21 4.56 1.16 0.22 3.75 1.47 0.22 3.38 1.47
Oil* vol. 0.23 4.91 -1.87 0.28 3.71 -1.99 0.29 3.34 -1.92
Gas* vol. 0.25 4.74 0.12 0.30 3.59 0.08 0.31 3.24 0.11
CRB* vol. 0.24 4.63 0.66 0.27 3.49 0.51 0.28 3.15 0.46
Oil vol. 0.24 4.78 -1.57 0.28 3.57 -1.39 0.29 3.21 -1.16
Gas vol. 0.25 4.74 0.08 0.30 3.59 0.05 0.31 3.24 0.09
CRB vol. 0.23 4.62 0.79 0.24 3.66 0.99 0.25 3.30 0.97
Wounded 0.10 0.17 0.39
TA-100 vol. 0.24 4.37 -0.73 0.28 2.06 -0.09 0.16 0.24 0.65
TA-100 spread 0.25 4.26 0.15 0.35 2.11 -0.27 0.55 0.09 -1.40
USD/ILS vol. 0.21 4.16 0.97 0.26 2.22 1.20 0.24 0.24 1.09
Oil* vol. 0.26 4.69 -2.50 0.36 2.26 -2.05 0.25 0.26 -0.84
Gas* vol. 0.28 4.41 0.17 0.39 2.17 0.15 0.39 0.17 0.76
CRB* vol. 0.26 4.27 0.08 0.35 2.12 -0.21 0.31 0.22 -0.71
Oil vol. 0.27 4.48 -1.84 0.37 2.16 -0.97 0.32 0.24 -0.22
Gas vol. 0.28 4.40 0.14 0.39 2.17 0.12 0.40 0.18 0.70
CRB vol. 0.24 4.24 0.52 0.30 2.16 0.61 0.29 0.23 0.45
Total 0.10 0.16 0.33
TA-100 vol. 0.24 4.50 -0.66 0.28 2.29 -0.10 0.21 0.50 0.62
TA-100 spread 0.24 4.39 0.23 0.33 2.36 -0.07 0.52 0.37 -1.32
USD/ILS vol. 0.21 4.30 1.00 0.25 2.48 1.24 0.26 0.52 1.08
Oil* vol. 0.25 4.80 -2.37 0.35 2.52 -2.10 0.32 0.53 -0.98
Gas* vol. 0.27 4.54 0.15 0.37 2.42 0.12 0.42 0.45 0.66
CRB* vol. 0.25 4.40 0.20 0.33 2.35 -0.09 0.35 0.48 -0.56
Oil vol. 0.26 4.60 -1.82 0.35 2.40 -1.06 0.37 0.51 -0.31
Gas vol. 0.27 4.54 0.12 0.38 2.42 0.09 0.43 0.45 0.61
CRB vol. 0.23 4.37 0.58 0.29 2.40 0.67 0.32 0.50 0.47
Number 0.12 0.14
TA-100 vol. 0.28 1.11 0.63 0.29 1.45 0.22
TA-100 spread 0.26 1.08 -1.24 0.33 1.47 -1.17
USD/ILS vol. 0.27 0.88 0.93 0.28 1.30 0.62
Oil* vol. 0.27 1.12 -1.16 0.32 1.61 -1.60
Gas* vol. 0.29 1.08 0.81 0.34 1.45 0.63
CRB* vol. 0.26 1.03 -0.56 Legend 0.33 1.49 -0.67
Oil vol. 0.29 1.12 -0.59 1% 0.35 1.58 -0.84
Gas vol. 0.29 1.08 0.76 5% 0.34 1.46 0.58
CRB vol. 0.29 1.04 0.29 10% 0.33 1.46 0.01
Table 4: Predictive ability tests of 1-year forward and expert USD/ILS forecasts.
Note: CP A is the p-value of the conditional predictive ability test when only a constant is
included. terror is the t-test of the terrorism variable considered. control is the t-test of the
control variable considered. A positive t-test means that expert forecasts are better than market-
based ones. A negative t-test means the opposite. T otal is the sum of those killed and wounded,
and Number is the quantity of terrorist attacks. Variables with are in USD.
Figure 9: Encompassing test statistic (solid) and the critical value (dashed) for
USD/ILS. Note: Signi…cance level: 5%. See Fig. 3 for an explanation of the labels.
Given that the variation in forecast performance is quite explicable (not ran-
dom), we believe that it is the lack of power of the ‡uctuation test in small samples,
rather than the constant relative performance, that causes us to fail to reject the
null hypothesis.
Table 4 indicates that the USD/ILS forecast performance is strongly related
to terrorism data. Surprisingly, the NII data now seem clearly less powerful in
explaining forecast performance. In particular, the number of attacks, which out-
performed all other measures for ination, is now completely void of predictive
power. On the contrary, the number of people killed now plays a major role. The
reason might be that— by its nature— international traders are much more active
in the exchange market than they are in the Israeli bond market. Due to the large
number of attacks in Israel, low-casualty terrorism barely receives international
mention these days. This might explain why the number of casualties (i.e., people
killed in attacks) is much more important for the exchange market.
As terrorism has a clear and identi…ed impact on …nancial and real asset mar-
kets (Zussman et al., 2008; Elster et al., 2017), our …nding adds to the literature
by showing that terrorism has a substantial impact on forward markets, involving
a signi…cant loss in predictive ability.
Table 4 shows that the Tel Aviv Stock Exchange (TASE) spread, oil volatility
in USD and ILS, and CRB volatility in USD t-tests are generally negative over
all our terrorism indicators, indicating that expert forecasts, relative to market
forecasts, performance deteriorates in times of uncertainty (i.e., when the terrorism
indicator’s t-test is high).
5 Interpretation
Expert forecasts of in‡ation are superior to market-based forecasts during periods
of high uncertainty. The result is qualitatively similar for exchange rate forecasts.
In both cases, we …nd strong evidence that it is indeed terrorism, rather than
related phenomena (e.g., commodity price ‡uctuations and …nancial distress), that
triggers changes in forecast performance. Moreover, we …nd in both cases that the
performance of market forecasts deteriorates more strongly.
These results are not surprising. As terrorist attacks a¤ect agents’behavior,
market and economic expectations change accordingly. Expert and market fore-
casts are then a¤ected in several ways. First, such changes increase forecasting
bias and errors. The drastic impact of terrorism seriously a¤ects market player
and forecaster perceptions, which in turn inuence their implied forecasts. Second,
such events substantially a¤ect the predictive ability of these forecast providers
and their updates following the events, at least for the Israeli ination and ex-
change rate (USD/ILS) forecasts.
However, the impact on relative performance indicates that there is more to
the storyan additional fundamental variable that adds some uncertainty. It seems
that these events disturb the predictive ability of market participants considerably
more than they a¤ect professional forecasters. Again, this is hardly surprising. It
makes sense that market participants, in a wider sense, are more a¤ected by the
psychological impact of uncertainty than are experienced professionals.
There are many potential reasons why market forecasts respond di¤erently
than professional forecasts. Although a large share of investors (e.g., investment
banks) pay attention to the in‡ation forecasts of professional forecasters, who in
turn also keep an eye on the market, the behavior of professional forecasters is
singular. Indeed, their objective functions (e.g., in terms of risk aversion) and the
time to consider updates of their forecasts lead to signi…cant di¤erences compared
to the market-based forecasts. An additional factor–information asymmetry–also
contributes to explaining the e¤ect on exchange rate forecasts. Exchange-rate
forecasts are supposedly more closely related to international markets, and foreign
participants may have derent news sources or even preferences.
Interestingly, …nancial uncertainty is not signi…cant when controlling for terror-
ism, although terrorism remains robustly signi…cant when controlling for …nancial
uncertainty. In other words, terrorism has a major role in the bias and predictive
ability of both expert and market-based forecasts. Interpreting these forecasts
without considering the current terrorism situation could lead to severe mistakes
for decision makers.
In line with the recent literature about the impact of oil prices on the US,
French, and UK ination forecasts (Badel and McGillicuddy, 2015; Bec and De Gaye,
2016), we show that, for Israel, exchange rate volatility and commodities volatility
matter in explaining in‡ation forecast errors (without considering terrorism data).
However, our results suggest that in the case of Israel, terrorism has a strong ex-
planatory power for both expert and market-based forecast errors compared with
other control variables (Section 2.3).
Both terrorism and the volatility of natural gas prices have signi…cant e¤ects on
the exchange rate forecasts. This link is a consequence of the three ways in which
natural gas has in‡uenced the Israeli economy since the 2000s. In the late 1990s,
the Israeli government decided to encourage the use of natural gas for several
reasons (environmental, cost, and resource diversication). From 2000 to 2016,
Israeli natural gas consumption multiplied more than 10-fold (from 0.01 to 0.12
quadrillion BTU). Thus, Israel became an important natural gas consumer. At the
same time, several natural gas reserves were discovered in Israel, making Israel one
of the 30 natural gas-exporting countries in the world in 2016. Before this, Israel
acquired natural gas through Egyptian pipelines in the Sinai Peninsula, which were
frequently targeted by terrorist organizations (e.g., during the August 2012 Sinai
attacks). The prominent link between natural gas price volatility and USD/ILS
forecasts resides in the Bank of Israel’s program to o¤set the e¤ects of natural gas
production on the foreign exchange rate by purchasing foreign currency.
Another layer was added to the Bank of Israel’s exchange rate policy in May
2013, following rapid appreciation of the nominal e¤ective exchange rate of ILS
(about 11.5% between September 2012 and May 2013). At the beginning of the
process, the appreciation was in‡uenced by the geopolitical situation, due to the
dissipation of tension prevalent at the beginning of the period. As the start date
for natural gas production from the Tamar site drew closer, the foreign exchange
market started to price in the expected return from decades of natural gas pro-
duction. The Bank of Israel reacted by launching its natural gas o¤set program.
These three factors are strongly related to imported in‡ation and the exchange
rate (USD/ILS). As the Bank of Israel tried to o¤set the appreciation of the Shekel
due to natural gas production and exports, natural gas price volatility became a
robust control variable, considering Israeli ination as well as USD/ILS exchange
rate forecasts.
As mentioned before, the implied market forecasts are actually a combination
of market expectations and risk premia.39 While we aim to control for that e¤ect,
we can only partly do so; that is, we can account for the ination risk itself (Section
4.1). Other factors that might be a¤ected in times of economic uncertainty, such
as liquidity risk, have to be omitted due to lack of data.40
6 Policy implications
Like the central banks of other developed economies, the Bank of Israel uses
market-based as well as expert forecasts to back its own forecasts used for mone-
tary policy decisions. These forecasts are also of prime importance because of their
utilization: they are presented and discussed by the MPC and by decision makers
39 Including the liquidity premium re‡ecting the di¤erent liquidity of nominal and in‡ation-
linked government bonds.
40 Greater war risks narrow the breakevens and a¤ect many other …nancial asset prices
(Rigobon and Sack, 2005). Narrowing of the US breakevens during the global …nancial crisis
represented an investor preference for the liquidity of nominal government bonds (Fleckenstein
et al., 2014; P‡ueger and Viceira, 2016). The di¤erence between forward exchange rates and
actual physical expectations (foreign exchange risk premium) could also represent compensation
for facing disaster risk (Farhi et al., 2009), reinforcing our conclusions for Israel.
and institutions in Israel and abroad (e.g., the International Monetary Fund, Or-
ganisation for Economic Co-operation and Development, and the World Bank).
From a policy perspective, it is thus of minor importance whether our market
forecasts are truly forecasts in the original sense of the word. What matters is
that they are treated as such by policymakers despite their shortcomings.
Our results show that using or interpreting these forecasts (expert and market-
based), without considering terrorism in Israel can cause an incorrect perception
of their current predictive power. Underestimating forecast errors and correspond-
ingly overestimating predictive ability could lead to severe over-reliance on incor-
rect forecasts. Consequently, when establishing their ination and exchange rate
projections, these institutions and decision makers should interpret expert and
market-based forecasts di¤erently, conditional on the current level of terrorism.
For example, between 2008Q4–2010Q3, these institutions should have considered
a persistent and robust bias in expert and market-based forecasts of ination.41
While prior literature and policy discussions have considered economic factors
that in‡uence in‡ation expectations (and thus, the forward-looking estimates of
the in‡ation gap), such as …nancial instability and commodity prices, these factors
have not included terrorism despite its apparently far greater importance.
The same argument holds true for exchange rate forecasts. During the last few
decades, Israeli monetary policy was strongly in‡uenced by the USD/ILS exchange
rate. Although some new challenges have emerged, the exchange rate still has
an impact on the monetary policy decision process. Thus, when policymakers
evaluate the future path of the Israeli economy and consider the market-based
and expert exchange rate forecasts, they should also consider the current terrorism
situation to avoid bias or low predictive ability in the forecasts they use.
Risk matters to some degree when considering breakeven in‡ation as a measure
of expectations.42 That said, terrorism still seems to a¤ect the actual underlying
expectation. This result should be considered when the MPC assesses in‡ation
forecasts. For instance, during the period September 2005 to August 2006 rockets
were launched from Gaza and Lebanon (Period T1). During this time, in‡ation
expectations rose and as a consequence of that policy makers dealt with possible
transmission from in‡ation expectations to actual in‡ation, calling for a rise in
interest rates. During that period interest rates had been risen by 2 percent, while
actual in‡ation remained approximately constant. The lesson from this paper is
that the extent of that rise shall be discussed in real time under the light of a
41 Flexible in‡ation targeters, such as the Bank of Israel, present their monetary policy objec-
tives in terms of the path of the in‡ation gap they are willing to tolerate following a cost-push
shock until the economy moves back to the in‡ation target. In practice, central banks formulate
the normative trade-o¤ between in‡ation and output variability in this natural and intuitive
way, thus improving communication with the general public (Cukierman, 2015). This in‡ation
gap makes use of market-based and expert in‡ation forecasts to make a decision today on the
monetary policy rate to be implemented tomorrow. Therefore, underestimating forecast error
and overestimating predictive ability performance during periods of terrorism could result in
errors/bias in critical monetary policy decisions.
42 Our …nding that terrorism a¤ects market-based in‡ation forecasts remains robust when
controlling for in‡ation risk. Nevertheless, we …nd that risk measures have a substantial impact
on breakeven in‡ation forecasts.
possible bias in in‡ation expectations. Terrorism may have reinforced this bias.
When policymakers evaluate the future path of in‡ation and the exchange
rates in Israel, they should give higher weight to expert forecasts during periods
of terrorism. However, the encompassing tests suggest that market-based fore-
casts should not be dismissed entirely, as they do provide some information. The
di¤erence in mean di¤erence of squared errors is 1.86 in times of high terrorism
(above median), and 0.31 in times of low terrorism (below median). In short, ex-
pert forecasts are better in both regimes. Still, there can be a bene…t to including
the worse forecast if it is not encompassed. However, this seems to be mostly the
case in times of extremely high terrorism as in the early part of our sample. Given
that this period is inevitably part of the initial training period of a pseudo out of
sample forecasting exercise, we are unable to quantify the bene…ts using our fairly
short sample.
Our …ndings provide two practical solutions for the policymaker concerning
decisions using forecasts extracted after terrorist attacks. The …rst is to assess the
forecasts published just before the terrorist attack, i.e., the forecasts prevailing in a
period that was not subject to terrorism, and to give them greater emphasis in the
current decision process compared to the forecasts published in times of terrorism.
The second is to prefer expert-based rather than market-based forecasts in this
7 Conclusion
As a small, developed, open economy implementing an in‡ation-targeting mone-
tary policy in the context of …nancial instability and terrorism, Israel remains the
best laboratory for analyzing in‡ation and exchange rate forecasts.
The consequences of terrorist attacks on expert as well as market-based forecast
performance are absent from the literature. This study …lls that gap by showing
that terrorism had a signi…cant impact on in‡ation and exchange rate forecast
errors in Israel over the last 15 years. Moreover, under all types of uncertainties
analyzed, expert in‡ation forecasts are generally better than breakeven (market-
based) ination forecasts.
When considering the number of terrorist attacks, the picture is very clear:
whatever the type of in‡ation forecast, the number of terrorist attacks has the best
explanatory power for the relative predictive ability of the forecasts we consider.
Terrorism has a signi…cant impact on both components of market-based in‡ation
forecasts (in‡ation forecast as well as its risk premium), even if agents are not risk
We also show that oil and TASE-100 control variables are sometimes found to
ect in‡ation forecasts, a result in line with Bec and De Gaye (2016), although
this …nding depends strongly on the terrorism indicator included in the model.
Exchange rate forecasts are more strongly a¤ected by the number of fatalities
from terrorist attacks, whatever the quantitative methodology for accounting for
them. We relate this to the fact that external market participants in the exchange
market give higher weight to attacks in which human lives are lost.
Uncertainty in general, and terrorism in particular, alters the forecasting per-
formance of market participants much more than professional forecasters’one. At
least in the case of Israel, the weak average performance of market participants
seems to be mostly driven by those episodes.
Policymakers should pay attention to market-based forecasts and prefer expert
forecasts during periods of terrorism. Forecastersexperience and low-frequency
of updating, i.e., less subject to overreaction, could play a role in their superior
predictive ability compared with market-based forecasts.
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... There has been increasing interest in professional economic forecasts in recent decades, partly due to the increasing importance of expectations in modern workhorse macroeconomic models. In a few of the most active …elds, authors have compared private-sector and central bank professional forecasters (Romer and Romer, 2000;El-Shagi et al., 2016), and professional forecasts to market-implied forecasts (Adeney et al., 2017;Benchimol and El-Shagi, 2020). Others have assessed whether forecasts (or forecast spreads) incorporate information regarding macroeconomic uncertainty (Bachmann et al., 2013;Bloom, 2014;Rossi and Sekhposyan, 2015). ...
... First, economic agents, including policymakers such as central banks, strongly rely on forecasts (Piotroski and Roulstone, 2004). Since expert forecasts are generally better than market-based forecasts (Adeney et al., 2017;Benchimol and El-Shagi, 2020), the economic agents mostly rely on the average of expert forecasts 1 (Genre 1 Although it is impossible to certify if the Fed decides according to expert forecasts, it is interesting to know how often the Fed often mentions expert forecasts. However, we can reasonably assume the Fed actively considers expert forecasts since the GFC, among other indicators, in their decision and communication processes. ...
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Each person's characteristics may influence that person's behaviors and their outcomes. We build and use a new database to estimate experts' performance and boldness based on their experience and characteristics. We classify experts providing inflation forecasts based on their education, experience, gender, and environment. We provide alternative interpretations of factors affecting experts' inflation forecasting performance, boldness, and pessimism by linking behavioral economics, the economics of education, and forecasting literature. An expert with previous experience at a central bank appears to have a lower propensity for predicting deflation.
... Rossi and Sekhposyan (2016) consider the window size of 60 observations as reference and 25 observations as robustness. Rossi and Sekhposyan (2016), Benchimol and El-Shagi (2017), El-Shagi et al. ...
Our objective is to test the rationality of the forecasters for Brazilian inflation and we analyze the relationship of rationality with the macroeconomic and electoral variables. We use Survey of Professional Forecasters (SPF) for next month's inflation with monthly data. We consider times series and panel data traditional tests as Mincer and Zarnowitz (1969) and West and McCracken (1998) to verify if forecast errors have zero mean and are uncorrelated with variables available at the time the forecast is made. We use Rossi and Sekhposyan (2016) test with their asymptotic critical values and finite sample adjusted distribution critical values of El-Shagi (2019) to consider the possibility of instability. Also we consider the possibility of the forecasters loss function is asymmetric following Elliott et al. (2005) and whether forecasters adjust manually their forecasts based on their personal expertise according to Franses (2021). We reject forecast rationality with panel data or time series (consensus) using traditional tests. We do not reject the null hypothesis of rationality for the consensus inflation forecast if we use fluctuation rationality test. We obtain that forecasters have bias in inflation forecasts in the easing and tightening periods of monetary policy or election periods with panel data. But we have that economic cycle, monetary policy or election do not affect the rationality test with panel data. The consensus forecast seems to neutralize the bias of individual forecasts comparing with panel data and it reduces irrationality only for periods of recession, monetary policy tightening and without election. We obtain an asymmetric loss function mainly for institutions and we can reject forecast rationality by incorrectly considering the loss function as symmetric. Also we obtain that if the forecasters use an econometric model, they all adjust their model forecasts.
... Since the method is fairly new, and has only recently been made available for a standard statistical application (Stata), there are only few applications available at the moment, such as Benchimol & El-Shagi (2017). However, given the huge increase in interest in professional forecasts, its potential is huge and it is bound to be a staple tool of applied research in forecasting. ...
The importance of expectations in modern macroeconomic models and in particular of policy makers expectations for forward looking policy rules has generated a lot of interest in time series of professional forecasts (including central bank staff forecasts). This has spawned a large literature on the evaluation of forecasts that are not model based or where the model is unknown. Although, the available time series of historical forecasts are typically short, this literature has so far mostly disregarded the small sample properties of the proposed tests and estimators. In this paper we fill this gap in the literature, focusing on a set of recently proposed rationality tests for unstable environments. Using a Monte Carlo study we demonstrate that the asymptotic tests are substantially oversized in finite samples including any sample size that is practically available. We provide finite sample adjusted critical values, that allow those tests to be properly applied to sample sizes of typically available forecasts such as the Survey of Professional Forecasters, the Federal Open Market Committee. The critical values we provide will help to avoid false rejections using those data.
This paper provides a novel investigation into how currency excess returns react to terrorist attacks. We construct a terrorism risk factor and demonstrate that it significantly matters to excess returns of both carry trade and individual currencies. Furthermore, we form a currency portfolio by simply buying terrorism-sensitive currencies and selling less terrorism-sensitive currencies, which yields economically positive and statistically significant returns. It has shown that this newly proposed terrorism risk factor could provide a marginal improvement for exchange rate pricing facing terrorist attacks.
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Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, a factor that should be considered by decision-makers. We focus on terrorism as a mostly endogenously driven form of political uncertainty and assess the forecasting performance of market-based and professional inflation and exchange rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, the performance of both market-based and expert forecasts is significantly worse during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange rate forecasts.
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We study the impact of fluctuations in global oil prices on domestic inflation using an unbalanced panel of 72 advanced and developing economies over the period from 1970 to 2015. We find that a 10 percent increase in global oil inflation increases, on average, domestic inflation by about 0.4 percentage points on impact, with the effect vanishing after two years and being similar between advanced and developing economies. We also find that the effect is asymmetric, with positive oil price shocks having a larger effect than negative ones. The impact of oil price shocks, however, has declined over time due in large part to a more credible monetary policy and less reliance on energy imports. We further examine the transmission channels of oil price shocks on domestic inflation during the recent decades, by making use of a monthly dataset from 2000 to 2015. The results suggest that the share of transport in the CPI basket and energy subsidies are the most robust factors in explaining cross-country variations in the effects of oil price shocks during the this period.
The importance of expectations in modern macroeconomic models and in particular of policy makers expectations for forward looking policy rules has generated a lot of interest in time series of professional forecasts (including central bank staff forecasts). This has spawned a large literature on the evaluation of forecasts that are not model based or where the model is unknown. Although, the available time series of historical forecasts are typically short, this literature has so far mostly disregarded the small sample properties of the proposed tests and estimators. In this paper we fill this gap in the literature, focusing on a set of recently proposed rationality tests for unstable environments. Using a Monte Carlo study we demonstrate that the asymptotic tests are substantially oversized in finite samples including any sample size that is practically available. We provide finite sample adjusted critical values, that allow those tests to be properly applied to sample sizes of typically available forecasts such as the Survey of Professional Forecasters, the Federal Open Market Committee. The critical values we provide will help to avoid false rejections using those data.
We investigate the information content of financial variables as signalling devices of two abnormal inflationary regimes: (1) low inflation or deflation, and (2) high inflation. Specifically, we determine the information content of equity and house prices, private credit volumes, and sovereign and corporate bond yields, for 11 advanced economies over the past three decades, using both signalling extraction and logit modelling. The outcomes show that high asset prices more often signal high inflation than low inflation/deflation. However, in some countries, high asset prices and low bond yields are a significant indicator of low inflation or deflation as well. The transmission time of financial developments to inflation can be quite long (up to 8 quarters). For monetary policy, these findings imply that stimulating asset prices through Quantitative Easing (QE) can effectively influence inflation, but that the effects are quite uncertain, both regarding timing and direction.
This paper proposes an empirical investigation of the impact of oil price forecast errors on inflation forecast errors for three different sets of recent forecast data: the median of SPF inflation forecasts for the United States and the Central Bank inflation forecasts for France and the United Kingdom. Mainly two salient points emerge from our results. First, there is a significant contribution of oil price forecast errors to the explanation of inflation forecast errors, whatever the country or the period considered. Second, the pass-through of oil price forecast errors to inflation forecast errors is typically multiplied by around 2 when the oil price volatility is large.
This paper relies on the variation of terror attacks across time and space as an instrument to identify the causal effects of terrorism on the preferences of the Israeli electorate. We find that the occurrence of a terror attack within three months of the elections is associated with a 1.35 percentage points increase on the local support for the right bloc of political parties out of the two blocs vote. This effect is of a significant political magnitude given the level of terrorism in Israel and the fact that its electorate is closely split between the right and left blocs. Moreover, a terror fatality has important electoral effects beyond the locality where the attack is perpetrated, and their electoral impact is stronger the closer to the elections they occur. Interestingly, the observed political effects are not affected by the identity of the party holding office. These results provide empirical support for the hypothesis that the electorate shows a highly sensitive reaction to terrorism, and substantiate the claim that terror organizations especially target democratic regimes because these regimes are more prone to make territorial concessions.
This paper explores the economic costs of conflict using a unique experiment. We analyze the effects of Hezbollah’s massive surprise rocket attack against northern Israel during the 2006 Second Lebanon War and the continued threat posed by the organization’s expanding rocket arsenal on the housing market, the labor market and patterns of migration flows and sorting. Relying on hedonic and repeat sales approaches and using a difference-in-differences identification strategy for 2000-2012, we show that the attack led to a 6-7 percent decline in house prices and rents in the most severely hit localities relative to other localities in northern Israel. These effects persisted until 2012, suggesting that the public continued to view the rocket threat as credible. In contrast, we find practically no effect on labor market conditions, migration flows and sorting.
While the relation between terrorism and tourism has been an important topic for tourism research, the questions whether terrorism affects tourism immediately and how long after a terrorism event tourism recovers are, as yet, not clearly answered. The aim of this article is to better understand the magnitude and temporal scale of the impact of terrorism on tourism. To this end, a research model differentiating between short-term and long-term effects of terrorism on tourism is developed and analyzed for the destination Israel using data on tourists from Germany. The results show both short-term and long-term impacts with a time lag between the terrorist event and the beginning of tourism decline of 1 or up to 6 months. An economic influence on the development of tourist arrivals was not detected, but seasonality plays an important role in the relationship between terrorism and tourism.
Two daily, real-time, real-activity indexes are constructed for the United States, euro area, United Kingdom, Canada, and Japan: (i) a surprise index summarizing recent economic data surprises and measuring optimism/pessimism about the state of the economy, and (ii) an uncertainty index measuring uncertainty related to the state of the economy. The surprise index parsimoniously preserves the properties of the underlying series when affecting asset prices. For the United States, the real-activity uncertainty index is compared to other uncertainty proxies to show that, when uncertainty is strictly related to real activity only, it has a potentially milder effect on economic activity.