Yield spread as a leading indicator of Tunisian industrial production

Abstract

Can the yield spread, which has been found to predict with surprising accuracy the movement of key macroeconomic variables of developed countries, also predict such variables for an emerging country. This paper is an attempt to answer empirically this question for the Tunisian economy. It also examines international financial linkages and how the euro area yield curve helps to predict domestic macro financial variables. Although the phenomenon has been widely examined in developed market economies, similar studies are virtually absent in the case of emerging economies. In part, this is because in developing economies with administrated interest rates, the yield curve has been either completely absent or not market determined and thus did not form a suitable test case. In the Tunisian financial market, there has been considerable improvement in terms of volumes, variety of instruments, numbers of participants and dissemination of information, and a yield curve particularly in case of government securities started emerging since 2000.
In our study, two approaches are implemented. The first one, widely used, consists in regressing the growth rate of the coincident indicator on the leading indicator. In the second one, we examine the usefulness of the yield spread in predicting whether or not the economy will be in recession in the future. So, in that particular case we use a Probit model. For both approaches we use the in-sample forecasting ability as well as the out-of-sample accuracy of the outcomes.
The results are somewhat tentative but consistent with the similar studies conducted in case of other countries. Findings of the study provide evidence that the yield curve could be considered as a leading indicator of real growth or recessions in Tunisian context, and consequently may be useful for both to private investors and to policy makes for forecasting purposes and, perhaps more importantly to understand the ongoing process of international financial integration.

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©TechMind Research, Canada 93 | P a g e
International Journal of Management Excellence
Volume __ No.__ Month Year
Yield spread as a leading indicator of
Tunisian industrial production
BEN ALI TAREK
Higher Institute of Business Administration, University of Gafsa, Tunisia
Tarek.benal@yahoo.fr
Abstract- Can the yield spread, which has been found to predict with surprising accuracy the movement of key
macroeconomic variables of developed countries, also predict such variables for an emerging country. This paper is an
attempt to answer empirically this question for the Tunisian economy. It also examines international financial linkages and
how the euro area yield curve helps to predict domestic macro financial variables. Although the phenomenon has been
widely examined in developed market economies, similar studies are virtually absent in the case of emerging economies. In
part, this is because in developing economies with administrated interest rates, the yield curve has been either completely
absent or not market determined and thus did not form a suitable test case. In the Tunisian financial market, there has been
considerable improvement in terms of volumes, variety of instruments, numbers of participants and dissemination of
information, and a yield curve particularly in case of government securities started emerging since 2000.
In our study, two approaches are implemented. The first one, widely used, consists in regressing the growth rate of the
coincident indicator on the leading indicator. In the second one, we examine the usefulness of the yield spread in
predicting whether or not the economy will be in recession in the future. So, in that particular case we use a Probit model.
For both approaches we use the in-sample forecasting ability as well as the out-of-sample accuracy of the outcomes.
The results are somewhat tentative but consistent with the similar studies conducted in case of other countries. Findings of
the study provide evidence that the yield curve could be considered as a leading indicator of real growth or recessions in
Tunisian context, and consequently may be useful for both to private investors and to policy makes for forecasting
purposes and, perhaps more importantly to understand the ongoing process of international financial integration.
Key words- yield spread; in-sample forecasting; out-of-sample forecasting; economic growth; recessions; leading
indicator; predictive content; linear regression; probit model.
1. Introduction
There is a significant amount of empirical evidence to
suggest that the asset prices are forwardlooking and,
consequently, constitute a class of potentially useful
predictor of macroeconomic variables
1
. The literature on
forecasting using asset prices has identified in particular
the yield spread. It’s the difference between long-term and
short-term interest rates. While there has been evidence of
association between yield spreads and real economic
activity in every case of developed economies,
predictability varies across the countries. It has been
suggested that country-wise variations in the predictive
power is on account of the differences in regulatory
regimes among the economies. Although the phenomenon
has been widely examined in developed economies,
similar studies are virtually absent in the case of emerging
1
For a recent review of the extensive literature on the historical and
international performance of asset prices as leading indicators, see for
example Stock and Watson (2003b): they provided a survey of 66
previous papers on this subject.
economies. In part, this is because in developing
economies with administrated interest rates, the yield
curve has been either completely absent or not market
determined and thus did not form a suitable test case.
After having granted the necessity of a financial
deepening, development of domestic debt security markets
in these economies in the very recent years reflects their
efforts to self-insure against ‘sudden stops’ and reversals
in international capital flows following the string of crises
of the 1990s (IMF 2006). From a macroeconomic
perspective indeed, domestic debt markets were seen by
policy makers in emerging countries as an alternative
source of financing to cushion against lost access to
external funding. Moreover, from a microeconomic
perspective, deeper domestic debt markets were expected
to help widen the menu of instruments available to address
currency and maturity mismatches, which reduces risks of
financial crises. For all these reasons, local authorities
have engaged in deliberate efforts to develop domestic
debt markets. Until 1986, the Tunisian financial system
was characterised by a highly regulated regime, which has

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since been gradually liberalized. By the mid-1990s, there
has been considerable improvement in terms of volumes,
variety of instruments, number of participants and
dissemination of information, and a yield curve
particularly in case of government securities started
emerging since 2000.
The present paper is an attempt to test the relationship
between the yield spreads and real economic activity in
Tunisian context. It is organized as follows: section I
explains the economic rationale behind observed
association between the yield spread and real economic
activity. Section II presents a survey of the literature on the
phenomenon under study. Section III sets out the empirical
results of our exercise conducted on the Tunisian economy
when we evaluate the explanatory power of several
different combinations of yield spreads, based on the long
rates of five and ten years, and the short rates of one year,
three months and one month, in their ability to explain
cumulative growth of real industrial production. We
compare also the explanatory power of the domestic
spreads with the one of foreign spread. Section V
concludes this study.
2. Yield spread as predictor of real economic
activity: theoretical rationale
According to Peel and Taylor (1998), it is a “stylised fact”
that the slope of the yield curve can be used as a leading
indicator of future economic activity. Therefore, this
section will not devote much time to reviewing the
relevant theoretical reasons that explain the observed
relationship between the yield spread and real economic
activity. There are at least three main reasons that explain
the relationship between the slope of the yield curve and
real economic growth and thus explain why the yield curve
might contain information about future growth or
recessions. In general, this relationship is positive and,
essentially, reflects the expectations of financial market
participants regarding future economic growth. A positive
spread between long-term and short-term interest rates (a
steepening of the yield curve) is associated with an
increase in real economy activity, while a negative spread
(a flattening of the yield curve) is associated with a decline
in real activity.
The first reason stems from the expectations hypothesis of
the term structure of interest rates. This hypothesis states
that long-term interest rates reflect the expected path of
future short-term interest rates. In particular, it claims that,
for any choice of holding period, investors do not expect to
realise different returns from holding bonds of different
maturity dates. The long-term rates can be considered a
weighted average of expected future short-term rates. An
anticipation of a recession implies an expectation of
decline of future interest rates that is translated in a
decrease of long-term interest rates. These expected
reductions in interest rates may stem from countercyclical
monetary policy designed to stimulate the economy
2
. In
addition, they may reflect low rate of returns during
recessions, explainable, among other factors, by credit
market conditions
3
and by lower expectation of inflation.
Indeed, the slope of the yield curve is calculated on
nominal interest rates
4
and therefore embodies a term
representing expected inflation. Since recessions are
generally associated with low inflation rates, assuming for
example that a downward Phillips-curve relationship
holds, this can play a role in explaining the expectation of
low rate of returns during recessions. Alternatively, if
market participants anticipate an economic boom and
future higher rates of return to investment, then expected
future short rates exceed the current short rate, and the
yield on long-term bonds should rise relative to short-term
yields according to the expectations hypothesis.
Another reason which explains the above relationship is
related to the effects of monetary policy. For example,
when monetary policy is tightened, short-term interest
rates rise; long-term rates also typically rise but usually by
less than the current short rate, leading to a
downwardsloping term structure. The monetary
contraction can eventually reduce spending in sensitive
sectors of the economy, causing economic growth to slow
and, thus, the probability of a recession to increase.
Estrella and Mishkin (1997) show that the monetary policy
is an important determinant of term structure spread
5
. In
particular, they observe that the credibility of the central
bank affects the extent of the flattening of the yield curve
in response to an increase in the central bank rate.
The third reason is given by Harvey (1988) and Hu (1993)
and it is based on the maximisation of the intertemporal
consumer choices. The central assumption is that
consumers prefer a stable level of income rather than very
2
Haubrich and Dombrosky (1996) call this the « policy anticipations
hypotheses ».
3
The authors show also that the monetary policy is not the only
determinant of the term structure spread. In fact, there is a significant
predictive power for both real activity and inflation. They demonstrate by
an empirical analysis that the yield curve has significant predictive power
for real activity and inflation in both the United States and Europe. See
Estrella and Mishkin (1997) for further details. Estrella (1997) presents
also a theoretical rational expectations model that shows how the
monetary policy is likely to be a key determinant of the relationship
between the term structure of interest rates and future real output and
inflation.
4
Although the theoretical linkage expressed in economic models is
between the real term structure and future economic activity, it’s the
relationship of the nominal term structure with economic activity that has
been engaged the attention of empirical researchers for the simple reason
that nominal term structure is so readily observable whereas the
computation of the real term structure requires the estimation of inflation
expectations of market participants. These expectations are not directly
observable. In this case, Plosser and Rouwenhorst (1994) pointed out that
one would expect the nominal term structure to forecast real activity
better if the term structure of expected inflation is flat and stable over
time rather than sloped and variable.
5
But as Dueker (1997) explains, this is depends on their assessment of
the size and duration of the recession’s effect on short-term interest rates.

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high income during expansion and very low income during
slowdowns. In a simple model where the default-free bond
is the only financial security available, if the consumers
expect a reduction of their income - a recession - they
prefer to save and buy long-term bonds in order to get
payoffs in the slowdown. By doing that they increase the
demand for long-term bond and that leads to a decrease of
the corresponding yield. Further, to finance the purchase of
the long-term bonds, a consumer may sell short-term
bonds whose yields will increase. As a result, when a
recession is expected, the yields curve flattens or inverts.
3. Survey of literature
Fama, as early as in 1986 and later Stambaugh in 1988
mentioned that term structure appears to predict real
economic activity though these were not supported by any
detailed statistical analysis
6
. The presented graphs show
that rise and fall in forward rates precedes economic
upswing and recession respectively. Since then a
significant amount of empirical evidence has been
conducted to test the existence of relationship between
yield spread and real economic activity. The literature on
term spreads uses different measures of yield spread
7
. The
adage that an inverted yield curve signals a recession was
formalized empirically, by a number of researchers in the
late 1980s, including Laurent (1988, 1989), Campbell
Harvey (1988, 1989), Stock and Watson (1989), Chen
(1991), and Estrella and Hardouvelis (1991). These studies
mainly focused on using the term spread to predict output
growth (or in the case of Harvey 1988, consumption
growth) using U.S. data. Of these studies, Estrella and
Hardouvelis (1991) provided the most comprehensive
documentation of the strong (in-sample) predictive content
of the spread for output, including its ability to predict a
binary recession indicator in probit regressions. This early
work focused on bivariate relations, with the exception of
Stock and Watson (1989), who used in-sample statistics
for bivariate and multivariate regressions to identify the
term spread and a default spread. Notably, when placed
within a multivariate model, the predictive content of the
term spread can change if monetary policy changes or the
composition of economic shocks changes (Smets and
Tsatsaronis 1997). Movements in expected future interest
6
According to A. Estrella (2005), the analysis of the behaviour of interest
rates of different maturities over the business cycle back at least to
Mitchell (1913), Kesel (1965) and Butler (1978).
7
Research on the United States business cycle has relied mostly on
interest rates for U.S Treasury securities. One reason is convenience: data
for maturities are available continuously for a long period. Another
reason is that the pricing of these securities is not subject to significant
credit risk premiums that, at least in principle, may change with maturity
and over time. For similar reasons, studies of other countries tend to use
data on national government debt securities. Rates on coupon bonds and
notes are most easily accessible, but researchers in many countries have
also produced zero-coupon rates, witch may directly matched with the
timing of forecasts. Some analysts have also used, at short-term rates, the
leading rates of the central bank or others rates of many market.
rates might not account for all the predictive power of the
term spread. For example, Hamilton and Kim (2002)
suggested that the term premium has important predictive
content for output as well.
For the studies which forecast recessions rather than a
quantitative measure of real output growth, Estrella and
Hardouvelis (1991) and Estrella and Mishkin (1998)
documented that the yield curve slope significantly
outperforms other indicators in predicting recessions,
particularly with horizon beyond one quarter. This forecast
is done estimating a probit model. Dueker (1997) confirms
this result using a modified probit model which includes a
lagged dependent variable. Built on these works, many
papers, on the one hand, give empirical results on the fact
that these evidences are present also in the major countries
of the European Union and, on the other hand, they try to
improve or change the model used to forecast recessions.
These papers include Bernard and Gerlach (1998), which
provide a cross-country evidence on the usefulness of the
term spreads in predicting the probability of recessions
within eight quarters ahead. Estrella and Mishkin (1997)
focus on a sample of major European economies (France,
Germany, Italy and the United Kingdom). Sédillot (2001)
provides an empirical evidence for France, Germany and
the U. S. Ahrens (2002) evaluates the informational
content of the term structure as a predictor of recession in
eight OECD countries. Stock and Watson (2003b) examine
the behaviour of various leading indicators before and
during the U.S. recession that began in March 2001.
Harvey (1991), Hu (1993), Davis and Henry (1994),
Plosser and Rouwenhorst (1994), Bonser-Neal and Morley
(1997), Kozicki (1997), Campbell (1999), Estrella and
Mishkin (1997), and Estrella, Rodrigues, and Schich
(2003), Moneta (2003), and Mehl (2006) generally
conclude that the term spread has predictive content for
real output growth in major OECD economies. Estrella,
Rodrigues, and Schich (2003) use in-sample break tests to
assess coefficient stability of the forecasting relations and
typically fail to reject the null hypothesis of stability in the
cases in which the term spread has the greatest estimated
predictive content (mainly long horizon regressions).
Additionally, Bernard and Gerlach (1998) and Estrella,
Rodrigues, and Schich (2003) provide cross-country
evidence on term spreads as predictors of a binary
recession indicator for seven OECD countries. Unlike
most of these papers, Plosser and Rouwenhorst (1994)
considered multiple regressions that include the level and
change of interest rates and concluded that, given the
spread, the short rate has little predictive content for output
in almost all the economies they consider.
These studies typically used in-sample statistics and data
sets that start in 1970 or later. Three exceptions to this
generally sanguine view are Davis and Fagan (1997),
Smets and Tsatsaronis (1997) and Stock and Watson
(2003a). Using a pseudo out-of-sample forecasting design,

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Davis and Fagan (1997) find evidence of sub-sample
instability and report disappointing pseudo out-ofsample
forecasting performance across nine EU economies. Smets
and Tsatsaronis (1997) find instability in the yield curve–
output relation in the 1990s in the United States and
Germany.
Our paper follows the path of these studies with the aim of
examining the forecasting ability of the yield spread in
predicting growth and recession in the Tunisian context.
The main object of our contribution is to carry out this
investigation at different segments of the yield curve and
testing therefore which specific spread is the best predictor
of industrial production in the Tunisian economy.
4. Data description
Yield data used for the study were derived from the series
of annualised yields of different maturity Treasury bonds
and money-market interest rates compiled at daily intervals
by the Central bank of Tunisia. The sample period is from
month 1, 2001 to month 9, 2006. From the daily yield
series, a series of month-end yields were extracted and
these month-end yields were averaged to drive a series of
monthly yields. The term spreads were computed from
monthly yield series. The purpose of transformation the
yield data to monthly series is to match the frequency of
Industrial production data which are available at monthly
intervals. In this article, the term spread
8
at time t, St, is the
observed difference between a selected long term-yield
YLt and a selected short-term yield SYt : St = LYt − SYt .
We consider the following list of spreads
9
:
 S1 = LY1 − SY1 where : LY1 is the annualised
yield of ten year and SY1 is the annualised yield
of one year;
8
Observe that the difference
tt YSYL 
is proportional to the
difference between the forward rate calculated from YLt et YSt ,
t
f
,
and YSt. The forward rate is defined as in Shiller, Campbell and
Schoenholtz (1983):
 
)( sL
tstL
tDD YSDYLD
f


, where DL is the duration of the bond
with L as maturity and DS is the duration of the
bond with S as maturity. The difference
tt YSf
is the correct
measure of the slope of the yield curve, but it is proportional to
 
).()/( : ttsLLtttt SYLYDDDSYfSYLY 
9
Since we subject our data to linear regression analysis we need to carry
out tests for stationarity because it has been well established that non-
stationarity data can produce spurious results. These tests are carried by
means of augmented Dickey-Fuller tests. The results indicates that all
spreads are integrated of order zero and there is no reason to be concerned
about the danger of obtaining spurious results on account of non-
stationarity in the regression analysis to follow.
 S2 = LY2 − SY2 where : LY2 is the annualised
yield of five year and SY2 is the annualised yield
of one month;
 S3 = LY3 − SY3 where : LY3 is the annualised
yield of ten year and SY3 is the annualised yield
of one month;
 S4 = LY4 − SY4 where : LY4 is the annualised
yield of ten year and SY4 is the annualised yield of
five year;
 SF = LYF − SYF where: LYF is the annualised
yield of five year and SYF is the annualised yield
of three month.
5. Methodology
The basic methodology used for testing forecasting power
is the linear regression model and the probit model. For the
linear model, measures of economic growth (Index of
Industrial Production, IIP) are regressed on the spread and
it takes the following form:
ttktt SG


.
,
Where
ktt
G,
is the annualised percentage continuously
compounded growth of IIP over k months, and it’s defined
as
 
tktktt IIPIIPkG log(log)/1200(
, 
.
kt
IIP
denotes the level of IIP during the month t + k and IIPt
denotes the level of IIP during the month t.
Regressions are carried out to test the explanatory power
of the yield spread in respect of industrial production
growth over a k months ahead. Our approach to evaluate
the explanatory power of the models is to use all available
observations for estimating the regression model and to
examine the statistical significance of the regression
coefficients and the within sample explanatory power of
the models considered
10
.
For the second type of regression
11
, we use a probit model
in which the variable being predicted is a dummy variable
Rt where Rt=1 if the economy is in recession in period t
and Rt = 0 otherwise. The probability of recession at time
10
An econometric problem that arises whenever the cumulative growth of
several months is forecasted in a time
series regression of this nature where the overlap of observations is
created is the autocorrelation of the regression error terms. When the
cumulative growth of k months is forecasted, the regression errors tend to
follow a moving average process of k-1. This results in inconsistent
estimates of the standard errors of the regression coefficients. A well-
known solution for this problem is to correct the variance-covariance
matrix for serial correlation up to order k-1 adopting the Newey and West
(1987) method. We have followed this procedure in all our regressions
involving insample forecasting estimates.
11
These two types of models may be compared in two dimensions:
accuracy and robustness. But there is evidence that the most accurate
binary models perform about as well as the linear regression (Estrella
2005).

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t, with a forecast horizon of k periods is given by the
following equation:
)()1Pr( 10 ktt XccR 


Where
(.)

is the cumulative standard density function,
and X is the set of explanatory variables used to forecast
the recessions.
6. Results and interpretations
6.1. The linear regression estimates
In measuring the term spread, the long term yield can be
selected from several alternative long term maturity yields
and likewise the short term yield can be chosen from
several alternative short yields available to us in the data
set. As forecasting tools, how do these different yield
spreads perform? Is there an optimal choice of spread that
would perform best for a particular forecasting horizon and
for a particular beginning point in the period of activity
forecasted? To answer these questions we examine the
predictive power based on several alternative measures of
yield spreads. Thus, Equation (1) is estimated for each
spread over the 2001: M1 – 2006: M9 time period and the
results of estimates are presented in tables 1, 2, 3, 4 and 5
in appendix.
We first examine the question of whether the choice
between the yields of one and three months matters in the
computation of the spread by comparing, in charts 1, the
explanatory powers of the following regressions:
tktt
tktt
SG
SG






3,
1,
The
2
R
from the regression equation measures the
proportion of the variation in real industrial production
growth that is explained by the yield spread. At shorter
horizons ( k ≤ 4months ahead) the one year yield does as
better as the one month yield since their R-bar squares for
these months are nearly the same. Given the shorter yield
we now examine which of the longer yields are more
effective by selecting in turn the five year and the ten year
for computing the yield spread:
tktt
tktt
SG
SG






3,
2,
In the same way, we compare the predictive powers of the
two equations (system 4) for horizons which exceed 6
months ahead. By comparing the explanatory powers
(Charts 1), the spread S3 is more effective than the S2. The
pattern in explanatory power suggests that explanatory
power improves when the maturity period of the long term
bond corresponds more closely with the forecasting
horizon.
Financial markets have become increasingly integrated
internationally and the nature of this integration and the
transmission channels are not always well understood. A
growing strand of literature has attempted to analyse
international financial spillovers
12
but has largely ignored
the slope of the yield curve. To this level the yield curve in
the euro area can be expected to have some predictive
content for growth in Tunisian economy. It can further be
expected to convey better information on the future impact
of common shocks, given that euro area debt security
markets are more liquid than emerging economy ones.
Last, the euro has an important role in the exchange rate
policy of our economy. This magnifies the pass-through
from euro area policy interest rates to our domestic interest
rates. In turn, this contributes to potential co-movements
between the slope of the yield curve in the euro area and
the Tunisian domestic slope of the yield curve. And
indeed, recent evidence from Frankel et al. (2004) and
Shambaugh (2004) suggest that countries that have a
pegged exchange rate follow base country interest rates
more than countries that have a float, in particular when
they have lifted capital controls. In other words, having
fixed exchange rates forces countries to follow the
monetary policy of the base country.
Against this background, we investigate the usefulness of
the French slope of the yield curve as a predictor of
domestic growth over k months ahead. To compare the
explanatory power of foreign spread and domestic spread
and test for the existence of international financial
linkages
13
, we estimate the following system of equation:
tktt
tFktt
SG
SG






3,
,
12
For example, Plosser and Rouwenhorst (1994), using time series
techniques, find evidence that the US slope of the yield curve helps
predict growth in both Germany and the U.K. (and vice versa)
significantly. Bernard and Gerlach (1998), using probit estimation, find
that the slope of the yield curve in the US and Germany helps predict
recessions in other G7 countries, the UK and Japan, in particular,
significantly. Those earlier contributions have two main features,
however. First, they have ignored inflation altogether. Second, and more
importantly, they have focused on a small number of industrial
economies. Yet, when it comes to the slope of the yield curve,
international financial linkages are also pronounced for emerging
economies. Their small economic size makes the US or the euro area a
possible determinant of their domestic inflation and growth.
13
This predictive content may stem from ( i ) the larger economic size of
the French comparatively to Tunisian one, which makes it an important
component of foreign demand; (ii) the deeper French debt security
market, which leads to a greater ability of its yield curve to convey
information on the future impact of common shocks; and (iii) the
prominent role played by the EURO in the exchange rate policy of
domestic economy, which magnifies interest rate pass-through.

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The explanatory powers relative to the first equation of this
system are always higher than ones relative to the second
equation. By considering all spreads, the French one has
in-sample forecasting an important information content for
future k months ahead (k=18, 24, 30, 36 and 40) and,
relatively to the international sector, it can be considered
as a good leading indicator for Tunisian activity. In order
to judge the overall performance of the forecasting
equation, Charts 1 and 2 plot the R-bar squares values
from estimating the forecasting equation 1 using the
industrial production growth as the measure of the change
in real economic activity. The
),4,3,2,1(
2FiRSi 
from
the estimation of equation 1 range from -1.54 to 17.3

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percent for i = 1, from -3.1 to 6.5 percent for i = 2 , from -
5.54 to 30 percent for i = 3 , from -3.5 to 24 percent for i =
4 and from -1 to 14 percent for i = F. Thus the explanatory
power d epends on yield spread considered and in general
it increases with the lengthening of the forecast horizon.
For the spread S3 , for example, the proportion of variation
in future real activity explained by this leading indicator is
beyond 15% for forecasting horizon exceeds seven
months, but less than 5% for very short-term forecasting
horizon. This note is valid for the remaining spreads but
the best leading indicators, following
2
R
, are S1 and S3.

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While the
2
R
provides an indicator of the explanatory
power of the spreads for real IP growth, the coefficient

from equation 1 measures how much real IP growth
changes following a onepercentage point change in the
yield spread. A positive

would imply a positive
relationship between the current yield curve and future
economic growth. That is, the larger the spread is between
long-term and short-term interest rates, the stronger real
growth will be in the future. The yield spreads are found to
have information content for future industrial production
growth. Moreover, the response of industrial production
growth is often positive, in line with expectations (i.e. a
steepening of the yield curve is associated with higher
expected growth). This is not always the case, however, as
suggested by the results for the spread S4 and in some
instances, estimated coefficients are unstable, switching
sign across forecast horizons.
Charts 3 and 4 provide estimates of
1

for the k months
ahead forecasts for each spread. The coefficient

is
positive in all estimation with the exception of that relative
to S4 (for very short forecasting horizon). The statistical
significance of

is indicated by a solid bar. For the
spread S2, the solid bar also show that this leading
indicator is a significant predictor of real economic growth
in 75% observations related to forecasting horizon ranges.
The charts 3 and 4 show that the numbers of observations
for witch the yield spreads are statistically significant
predictor of future industrial production growth increase
with the forecast horizon. In particular, the spreads S2 and
S3 are being significant since k = 6 and remain until k =
40.
Estimates of the

’s themselves from the equation 1
provide an indication of the economic significance of the
yield curve as a predictor of future real economic growth.
In particular, the coefficient

measures the change in
industrial production growth for a given one-percentage
point change in the yield spread.
For the yield spread S3, for example, the chart 8 chows
that a one-percentage-point increase in yield spread today
is associated with an annualized 3.74-percentage-point
increase in growth over the next six months, an annualized
4-percentage-point increase in growth over the seven
months, an annualized 3.85-percentage-point increase in
growth over the next eight months, an annualized 3.65-
percentage-point increase in growth over the next nine
months and an annualized 3.11-percentage-point increase
in growth over the next ten months. Hence a widening of
the yield spread would imply an increase in industrial
production growth. For example, if real economic growth
in the Tunisian industrial production was 3 percent, a
widening of S3 by one percent point would imply an
increase in industrial production to 6 percent (2 + 1×4, 02)
over the next seven months.
Together the results indicate that while the yield spread
does help explain future real IP growth for many spreads,
the strength of the predictive power varies by explanatory
variable. The explanatory power of the yield spread is
highest in the case of S1 and S3 and lowest for others
spreads (Each bar represents the beta coefficients from the
regression of future real industrial production growth on
the corresponding yield spread. Statistical significance is
indicated by a shaded bar. Source: see appendix and
author's calculations).
6.2. The probit model estimates
A somewhat different approach involves the prediction of
whether or not the economy will be in a recession K
months ahead. This type of exercise abstracts from the
actual magnitude of economic activity by focusing on the
simple binary indicator variable. Although this forecast is
in some sense less precise, the requirements on predictive
power are in another sense less demanding and may
increase the potential accuracy of the more limited
forecast. Empirically, we would like to construct a model
that translates the steepness of the yield curve at the
present time into a likelihood of a recession some time in
the future. Thus, we need to identify three components: a
measure of steepness, a definition of recession, and a
model that connects the two.
The approach we employ is a probit model equation,
which uses the normal distribution to convert the value of
a measure of yield spread steepness into a probability of
recession k months ahead. Following Estrella and
Hardouvelis (1991) and Estrella and Mishkin (1998), we
study the ability of the slope of the yield curve to predict
recessions in the Tunisian context. First, we estimate a
probit model to obtain a probability of recession in the
Tunisian economy between 1 and 7 months ahead. Then,
we improve the probit model using the modification
proposed by Dueker (1997). In order to analyse the
predictive informative content in different segments of the
yield curve we use five yield curve spreads as explanatory
variables. We plug, therefore, in the right side of the
equation (2) all the spreads listed in the first panel of Table
1 and we estimate the model
14
.
Defining what is a recession is fundamental for
constructing the binary time series t R. The National
Bureau of Economic Research (NBER) officially dates the
beginnings and ends of US recessions and it defines a
recession as “a significant decline in activity spread across
the economy, lasting more than a few months, visible in
industrial production, employment, real income and
wholesale ret ail trade”.
14
The model is estimated using a non-linear method (the Newton-
Raphson).

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Another issue is raised in analysing the goodness of fit. In
the classical regression model, the coefficient of
determination R2 is used as a measure of the explanatory
power of the regression model. It can range in value
between 0 and 1, with a value close to 1 indicating a good
fit. In this kind of model it is no more likely to yield a R2
close to 1
15
. To avoid this problem we use the measure of
fit proposed by Estrella (1998). It is a pseudo-R2 in which
the log-likelihood of an unconstrained model, Lu , is
compared with the log-likelihood of a nested model, Lc
16
c
Ln
cu LL )/2(
2)/(1R-pseudo 

A last potential problem stems from the serially correlation
of the errors. Since the forecast horizons are overlapped,
the prediction errors are in general autocorrelated. Thus,
we correct this problem using the Newey-West (1987)
15
See, for example, Estrella , A.[1998]
16
The constrained model comes from a model with c1, in equation (1), is
equal to zero. The log-likelihood in the case of the probit model is given
by
)Pr(ln)1()1(Prln ktttktt
ttXRRXRRL   
technique and presenting thus t-statistics calculating using
robust errors adjusted for the autocorrelation problem.
Table 1 (panel 1) presents the Pseudo-R2 calculated after
the estimation of a probit model using the different spreads
as explanatory variable and with lags ranging from 1 to 7
months. The highest pseudo-R2 is obtained with the
estimation of a probit model considering as predictor the
spread S3. In particular, the lag which presents the best fit
is k = 6.
In this case, the pseudo-R2 is 0.169 and the t- This result is
significant at the 5 percent level, and if we make a
comparison with the pseudo - R2 of the other spreads we
can draw the conclusion that the best recession predictor is
the spread S3 lagged six months. statistic is -2.585
17
.
Indeed, some other spreads have also a significant measure
of fit at 5 and 10 percent. The highest pseudo-R2 is
obtained with the estimation of a probit model considering
17
A value of 0:169 seems low if it is interpreted as an R2 , but also in
other empirical studies, the pseudo-R2 is not very large. For example,
Estrella and Mishkin (1998) yielded on U.S. data a value of 0:296 using
as predictor the spread 10-year minus 3-month lagged four quarters and
Frank Sédillot (2001) yielded on France data a value of 0.17 using the
same definition of spread lagged six months.

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as predictor the spread S3. In particular, the lag which
presents the best fit is k = 6. In this case, the pseudo-R2 is
0.169 and the t-statistic is -2.585. This result is significant
at the 5 percent level, and if we make a comparison with
the pseudo - R2 of the other spreads we can draw the
conclusion that the best recession predictor is the spread S3
lagged six months. Indeed, some other spreads have also a
significant measure of fit at 5 and 10 percent.
As explained above, the probit model allows us to estimate
the probabilities that the economy will be in recession in a
given month on the basis of the interest rate spread
observed some months before. Figure 6 presents an
example of these probabilities using the domestic spread
S3 lagged 6 months and the foreign spread SF lagged 2
months.
Ideally, the probability should be one in the recession
months (which are shaded in the figure) and zero
otherwise. This chart shows that the estimated probability
increases in the recession periods and remains low in the
non-recession months.
6.3. Probit model with a lagged dependent
variable
One of the main assumptions of the probit model is that the
random shocks are independent, identically distributed
normal random variables with zero mean. In this kind of
model the errors are generally autocorrelated. In traditional
time series approach we deal with this problem using an
autoregressive moving average filter. Here, since the
shocks are unobservable this technique is not more
available. Therefore, we adopt the solution proposed by
Dueker (1997) and Stock and Watson (2003b) to remove
the serial correlation by adding a lag of Rt (the indicator
variable of the state of the economy). Therefore, we allow
the model to use information contained in the
autocorrelation structure of the dependent variable to form
predictions. The probit equation with a lagged dependent
variable becomes:
)()1Pr( 210 ktktt RcXccR  


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Table 2 presents the results of the estimations of this
model using respectively as explanatory variable the
spread S1, S2, S3 and SF and with lags ranging from one
to six . The pseudo-R2 is now calculated in the same
manner as explained above with the exception that we can
have three different specifications. The unrestricted model
Lu is calculated using also the lag of Rt. The restricted
model Lc can come from a model with both c1 and c2 are
equal to zero, with only c2 is equal to zero or with only c1
is equal to zero.
In the first specification (first row of Table 2), the
restricted model is the same as the simple probit model and
therefore, it possible to compare this pseudo - R2 with the
value obtained estimating the simple probit model. Now,
the pseudo-R2 is 0:236 for S3 and the best recession
predictor was obtained with the spread S3 lagged six
months
18
. However, this measure is sensible to the fact that
we add another explanatory variable making thus the
comparison not really meaningful. In the second
specification (first row in Table 2), we test for the
informational content provided by the lagged dependent
18
For this case, the McFadden R-squared indicates the same result as the
pseudo-R2. This is valid for the remaining spreads.
variable in addition to the information embodied in the
spread
The measure of fit is significant for the most leading
spread at one to six months forecast horizon, in particular
for S3 (with k=6), suggesting that the lagged dependent
variable provides also important information. In the last
and most interesting case (last specification in first row of
table 2), we test for the information content which goes
beyond the information already contained in the
autoregressive structure of the binary time series. The lag
which presents the best fit is still k = 6 and the value of the
pseudo-R2 is 0.099, proving a good informative content of
the spread.
The estimated probabilities of recession obtained from
running this model give us the same pace of probability’s
curve indicating that in recession months there is an
important likelihood of future decline in industrial activity.
Considering in-sample forecasting, it seems that the use of
a lagged dependent variable helps to forecast historically
recessions in the Tunisian economy.
Therefore, a probit model modified with the insertion of a
lagged dependent variable appears somewhat preferable
than the standard probit model. One disadvantage with in-

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Volume 2 No. 1 October 2013
sample forecasts is that they allow the forecast to depend
on data which were not available at the time of the
forecast. As a result, the empirical results of the previous
section may provide a misleading indication of the true
ability of the yield curve to forecast real activity. By
contrast, an out-of-sample forecast uses only information
available to market participants at the time of the forecast.
Moreover, an in-sample forecast can always be improved
by adding a new explanatory variable, but that can lead to
an over fitting problem. To avoid a possible misleading
indication of the true ability of the term spread to forecast
a recession it is important to carry out an exercise of out-
of-sample forecasting. Specifically, forecasts for each
period are based on an estimate of equation (2) and (7)
using only data up to the previous period. For example, the
forecast for 2005:M1 is estimated using coefficients from
the regression estimated over the 2001:M1 to 2004:M12
period.
The quality of the out-of-sample forecast is evaluated
using the Root Mean Squared Error (RMSE) statistic. The
RMSE provides an estimate of the out-of-sample forecast
error, and hence measures the accuracy of the forecast. The
low the RMSE, the better the forecast. In evaluating the
out-of-sample forecast power of the yield spreads, the
RMSE from each yield spread forecast is compared with
the RMSE of alternative forecasts of industrial production
activity. Indeed, one advantage of the RMSE measure is
that, for a given country, it can be compared across
different forecasting models. In this section, the out-of-
sample predictive power of the yield spreads model is
compared with that of two alternative forecasting models
aver range horizon. In the first alternative, equation (2) is
used ( called m1). In the second one, equation (7) is
implemented (called m2), and in the third case we use a
benchmark equation which is simply the identical equation
(2) without the indicator variable and where past changes
in t R are used to predict future changes.
To determine the relative forecast performance of the three
models, the yield spread model, the lagged model and the
combined yield spread plus lagged model were estimated
across six forecast horizons and their relative out-of-
sample RMSE’s were compared for the three spreads: S1,
S3 and SF.. Relatively to the two models (m1 and m2), we
have three sets of RMSE for every horizon of forecasting.
Thereafter, we return the RMSE of the equation m1 to the
RMSE of the equation m3 and the RMSE of the equation
m2 in the RMSE of the equation m3. If the report is lower
to the unit, then the model m1 brings information in
relation to the model m3
19
. The same reasoning makes
itself for the model m2. Tables 6, 7 and 8 show the results
of these model comparisons (m1 and m2).
19
The relative RMSE compares the performance of a candidate forecast
to a benchmark forecast, where both are computed using the pseudo out-
of-sample methodology. See for example Stock and Watson (2001).
For the equation (7) in witch S3 is the leading indicator,
the model m1 outperforms the model m2 in 19 out of 30
cases and m2 outperforms m1 only 11 out of 30 cases.
Otherwise, there are 10 out of 30 cases where the relative
RMSE related to m1 is less than one. Whereas, there are
only 7 out of 30 cases in witch the relative RMSE related
to m2 is less than one. By considering the case of spread 2,
the model m1 outperforms the model m2 in 19 out of 30
cases and m2 outperforms m1 only 11 out of 30 cases. In
relation to m3, there are 73% cases where the relative
RMSE related to m1 and that related to m2 are less than
one and consequently the spread S1 is better than S2 as
regard to the out-of-sample forecasting based in equation
(7). Lastly, for the spread SF, the model m1 outperforms in
all cases the model m2 and in each case of out-of-sample
estimates, their relative RMSE are all less than one,
suggesting that SF dominate the others two spreads
concerning this criterion of robustness’ dimension.
7. Summary and conclusion
This article has provided evidence on the ability of mainly
Tunisian yield spreads to predict future real economic
activity. Several interesting and important results were
identified witch are broadly consistent with the results of
previous studies, but are also more comprehensive in that
they evaluate the predictive power of yield spread across
multiple segments of the Tunisian yield curve. The results
indicate the considering yield spreads are economically
significant predictor of economic activity. Explanatory
power begins to increase beyond five months for the
spreads ten year minus one month and ten year minus one
year, indicating that these two domestic spreads are the
best leading indicators for Tunisian industrial production.
In examining international financial linkages, the paper has
also assessed the ability of the slope of the French yield
curve to help predict growth in domestic activity. It has
found that the French spread five year minus three month
has information content in particular for long forecasting
horizon.
The empirical results of this study also show, in sample
estimates, that the strength of the relationship between the
yield spreads and future economic growth varies across the
different examined spreads. The predictive power is
strongest in the case of spread ten years minus one month
and in the case of French spread. Concerning the first
spread, it consistently explains, in average, roughly 15
percent of the variation in future industrial production for
forecasting horizon exceeding 6 month ahead. For the
second spread, it explains 14 percent of the variation in
future industrial production for forecasting horizon with 30
month ahead.
Considering the out-of sample forecasts, the results of this
paper show that the best predictor of recession is the
spread between 10-year and 1-month interest rates.
Therefore, this specific yield spread can be useful for

©TechMind Research, Canada 105 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013
economic and monetary policy purposes. To arrive to this
conclusion we used two non-liner model specifications to
forecast the probability of a recession in the Tunisian
economy. These are the standard probit model proposed by
Estrella and Mishkin (1998) and the modified probit model
with the addition of a lagged dependent variable proposed
by Dueker (1997). We found that the use of a lagged
dependent variable helps to forecast historically recessions
in domestic context. Specific attention was paid on the
accuracy of the forecast. We carried out an exercise of out-
of-sample forecasting to investigate the out-of-sample
performance of the probit models. The simple probit model
(with the spread 10-year minus 1-month as explanatory
variable) gives the best result at 6 months forecast horizon
and performs better than the remaining spreads. With the
addition of the lagged dependent variable in the probit
model (with same spread) the forecasting ability improves
significantly and beat the results related to a simple probit
model. The different results carried out show that the
spread 10-year minus 1-month could have provide useful
information both to private investors and to policy Makers.
REFERENCES
[1] Ahrens, R. (2002), « Predicting recessions with
interest rate spread: a multicountry regime-
switching analysis”, Journal of International
Money and Finance, Vol.21, pp. 519-537.
[2] Alles, L. (1995), « The Australian term structure
as a predictor of real economic activity », The
Australian Economic Review, 4th quarter.
[3] Ang A., M. Piazzesi and M. Wei (2006), “What
does the Yield Curve Tell us about GDP
Growth?”, Journal of Econometrics, 131, pp.
359-403.
[4] Artus, P. & Kaabi, M. (1993b), « Structure par
terme des taux d’intérêt et reprise économique »,
Document de travail, Caisse des Dépôts et
Consignations, n°1997-22/T, mars.
[5] Artus, P. & Kaabi, M. (1995), « Les primes de
risque jouent-elles un rôle significatif dans la
détermination de la pente de la structure des taux
», Document de travail, Caisse des Dépôts et
Consignations, n°1995-01/F, février.
[6] Berk, J. & Bergeijk, P. (2000), “Is the yield curve
a useful information variable for the eurosystem?”
European Central Bank, Working Paper, no.11.
[7] Bernard, A. & Gerlach, S. (1996), „Does the term
structure predict recessions? The international
evidence”, Working Paper, Bank for International
Settlements, n°37, September.
[8] Bernard, A. & Gerlach, S. (1998), “Does the
Term Structure Predict Recessions? The
International Evidence,” Int. J. Finance Econ.
3:3, pp. 195–215.
[9] Bliss, R.R. (1996) “Testing term structure
estimation methods”, Working Paper, Federal
Reserve Bank of Atlanta, 82, n°96-12, October.
[10] Bliss, R.R. (1997), « Movements in the term
structure of interest rates », Economic Review,
Federal Reserve Bank of Atlanta, 82, 16-33,
quatrième tremestre.
[11] Bonser-Neal, C. and T. Morley (1997), “Does the
Yield Spread Predict Real Economic Activity? A
Multicountry Analysis,” Federal Reserve Bank
Kansas City Economic Review, 82, 3, pp. 37–53.
[12] Brailsford, J. & Maheswaran, K. (1998), “The
dynamics of the Australian short-term interest
rate”, Australian Journal of Management, vol.23,
n°2, p.213-234.
[13] Campbell, J. Y. & Shiller , R. J. (1991) : « Yield
spread end interest rate movement: a bird’s eye
view », Review of Economic Studies, 58(3), May,
p.495-514.
[14] Campbell, John Y. (1999), “Asset Prices,
Consumption and the Business Cycle,” in the
Handbook of Macroeconomics, Vol. 1. John B.
Taylor and Michael Woodford, eds. Amsterdam:
Elsevier, pp. 1231–303.
[15] Chauvet, M., and S. Potter, (2001). “Forecasting
recessions using the yield curve," Federal Reserve
Bank of New York, Staff Reports, no. 134,
August.
[16] Chen, Nai-Fu. (1991), “Financial Investment
Opportunities and the Macroeconomy,” J.
Finance 46:2, pp. 529–54.
[17] David, N., Ayling, E. & Hodgkinson, L. (2003), «
An examination of the information role of the
yield spread and stock returns for predicting
future GDP », Applied Financial Economics,
n°13, p. 593-597.
[18] Davis, E. Philip and Gabriel Fagan. (1997), “Are
Financial Spreads Useful Indicators of Future
Inflation and Output Growth in EU Countries?”
J.Applied Econometrics 12, pp. 701–14.
[19] Davis, E. Philip and S.G.B. Henry. (1994), “The
Use of Financial Spreads as Indicator Variables:
Evidence for the United Kingdom and Germany,”
IMF Staff Papers 41, pp. 517–25.
[20] Dotsey, M. (1998), “The predictive content of the
interest rate term spread for future economic
growth”, Economic Quarterly, Federal Reserve
Bank of Richmond, issue Sum, pages 31-51.
[21] Dubois, E. & Janci, D. (1994), « Prévision du PIB
par la courbe des taux : une constatation
empirique en quête de théorie », Economie et
Prévision (112), 69-85.
[22] Dueker, J. (1997), “Strengthening the Case for the
Yield Curve as a Predictor of U.S. Recessions,”
Fed. Reserve Bank St. Louis Rev. 79:2, pp. 41–50.

©TechMind Research, Canada 106 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013
[23] Estrella A., A. Rodrigues and S. Schich (2003),
“How Stable is the Predictive Power of the Yield
Curve? Evidence from Germany and the United
States,” Review of Economic and Statistics, 85, 3,
August 2003, pp. 629-644.
[24] Estrella, A. & Hardouvelis, G.A. (1991), “The
term structure as a predictor of real economic
activity”, The Journal of Finance, 46, 555-576.
[25] Estrella, A. & Mishkin, F.S. (1995), “The term
structure of interest rates and its role in monetary
policy for the European central bank”, NBER
Working Paper, n°5279, September.
[26] Estrella, A. (2005a), “Why does the yield curve
predict output and inflation?”, The Economic
Journal (July): 722-44.
[27] Estrella, A. (2005b), “The Yield Curve as a
Leading Indicator: Frequently Asked Questions”,
Federal Reserve Bank of New York, October
2005.
[28] Estrella, A. and F. Mishkin (1997), “The
Predictive Power of the Term Structure of Interest
Rates in Europe and the United States:
Implications for the European Central Bank,”
European Economic Review, 41, pp. 1375–1401.
[29] Estrella, Arturo and Frederic S. Mishkin. (1998),
“Predicting U.S. Recessions: Financial Variables
as Leading Indicators,” Rev. Econ. Statistics 80,
pp. 45–61.
[30] Fama, E.F. (1986),“The information in the term
structure”, Journal of Financial Economics, 13,
509-528.
[31] Fama, E.F. (1990), “Term-structure forecasts of
interest rates, inflation, and real returns”, Journal
of Monetary Economics, 25, 59-76.
[32] Frankel J., S. Schmukler and L. Serven (2004),
“Global transmission of interest rates: monetary
independence and currency regime”, Journal of
Money and Finance, 23, pp. 701-733.
[33] Hamilton, D. & Kim , D. (2002), “A Re-
examination of the Predictability of Economic
Activity Using the Yield Spread”, Journal of
Money, Credit & Banking, May2002, Vol. 34
Issue 2, p340-360.
[34] Harvey, Campbell R (1989), “Forecasts of
Economic Growth from the Bond and Stock
Markets,” Finan. Analysts J. 45:5, pp. 38–45.
[35] Harvey, Campbell R. (1988), “The real term
structure and consumption Growth,” J. Finan.
Econ. 22, pp. 305–333.
[36] Haubrich, Joseph G. and Ann M. Dombrosky.
(1996), “Predicting Real Growth Using the Yield
Curve,” Fed. Reserve Bank Cleveland Econ. Rev.
32:1, pp. 26–34.
[37] Hu, Zuliu. (1993), “The Yield Curve and Real
Activity,” IMF Staff Papers 40, pp. 781–806.
[38] International Monetary Fund (2006), “Structural
changes in emerging sovereign debt and
implications for financial stability”, Global
Financial Stability Report, Avril 2006,
Washington D.C.
[39] Jorion, P. and F. Mishkin (1991), “A Multi-
Country Comparison of Term Structure Forecasts
at Long Horizons,” Journal of Financial
Economics, 29, pp. 59–80.
[40] Kozicki, S. (1997), “Predicting Real Growth and
Inflation with the Yield Spread,” Federal Reserve
Bank Kansas City Economic Review, 82, pp. 39–
57.
[41] Laurent, R. (1988), “An interest rate-based
Indicator of monetary policy,” Fed. Reserve Bank
Chicago Econ. Perspectives 12:1, pp. 3–14.
[42] Laurent, R. (1989), “Testing the Spread,” Fed.
Reserve Bank Chicago Econ. Perspectives 13, pp.
22–34.
[43] Mehl, A. and J. Reynaud (2005), “The
determinants of ‘domestic’ original sin in
emerging market economies”, ECB Working
Paper, No. 560, December 2005.
[44] Moneta, F. (2003), « Does the yield spread
predict recessions in the Euro area », European
Central Bank, Working Paper n. 294.
[45] Newey, W.K. and K.D. West (1987), “A Simple
Positive, Semi-Definite, Heteroskedasticity and
Autocorrelation Consistent Covariance Matrix”,
Econometrica, 55, pp. 703-708.
[46] Peel D.A.; Taylor M.P. (1998), “The slope of the
yield curve and real economic activity: tracing the
transmission mechanism”, Economics Letters,
Volume 59, Number 3, pp. 353-360(8).
[47] Plosser, C.I. and K.G. Rouwenhorst (1994),
“International term structures and real economic
growth”, Journal of Monetary Economics, 33, pp.
133-155.
[48] Sédillot, F. (2001), “La pente des taux contient-
elle de l’information sur l’activité économique
future?”, Economie et Prévision, n°. 147, pp. 141-
157.
[49] Shambaugh, J., (2004), “The effects of fixed
exchange rates on monetary policy” Quarterly
Journal of Economics, 119, 1, February 2004, pp.
301-352.
[50] Shiller, R. Campbell, J & Schoenholtz, K. (1983),
“Forward Rates and Future Policy: Interpreting
the Term Structure of Interest Rates”, Brookings
Papers on Economic Activity, Vol. 1983, No.
1.pp. 173-223.

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Appendix
Table 1: Predicting future change in Industrial Production using the yield Spread S1
Sample: Monthly, 2001M1 to 2006M9
Spread S1
k
(Months
ahead)
c1
c2
2
R
SEE
NOBS
1
0.028561
-0.326443
-0,01536
0.339292
67
(0.368452)
(-0.069924)
2
-0.005065
1.634079
-0,012631
0.155654
66
(-0.106411)
(0.534829)
3
-0.021254
2.533809
-0,004951
0.126886
65
(-0.521641)
(0.929559)
4
-0.008172
1.636745
-0,007111
0.090275
64
(-0.242405)
(0.738618)
5
-0.031451
2.967716
0,037973
0.065686
63
(-1.089138)
(1.629789)***
6
-0.041377
3.614858
0,08757
0.056728
62
(-1.811085)
(2.522006)*
7
-0.040329
3.597536
0,128601
0.047081
61
(-2.526773)
(3.821367)*
8
-0.035004
3.206183
0,15598
0.038087
60
(-2.666530)*
(4.075803)*
9
-0.033001
3.113668
0,126452
0.041569
59
(-2.838866)
(4.239253)*
10
-0.025297
2.590052
0,131807
0.034067
58
(-2.404132)
(3.883054)*
11
-0.021775
2.360778
0,11641
0.033316
57
(-2.065799)
(3.567261)*
12
-0.020789
2.305020
0,110936
0.033573
56
1.930094
(3.711107)*
18
-0.007439
1.524122
0,10694
0.023573
50
(-0.745732)
(2.844720)*
24
0.000836
1.050934
0,10534
0.017080
44
(0.103163)
(2.642508)*
30
0.009425
0.633171
0,071212
0.012749
43
(1.527080)
(2.180401)
36
0.011251
0.536042
0,141903
0.007915
32
2.732678)
(2.240930)*
40
0.007151
2.073985
0,172986
0.009095
28
(0.846952)
(2.748280)*
Notes: for this table and the following four ones, in parentheses are t-statistic after correction by
method of Newe and West (1987) of standard errors that take into account the moving average
created by the overlapping of forecasting horizons as well as conditional heteroskedasticity. Nobs.
denotes the number of monthly observations.
2
R
is the coefficient of determination adjusted for degrees of freedom, and SEE represents the
corrected regression standard error.
*,** and *** significantly different respectively at 5%, 10% and 20%.

©TechMind Research, Canada 108 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013

©TechMind Research, Canada 109 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013

©TechMind Research, Canada 110 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013

©TechMind Research, Canada 111 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013

©TechMind Research, Canada 112 | P a g e
International Journal of Management Excellence
Volume 2 No. 1 October 2013