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The importance of the financial system for the real economy

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This paper first describes financial variables that have been constructed to correspond to various channels in the transmission mechanism. Next, a Bayesian VAR model for the macroeconomy, with priors on the steady states, is augmented with these financial variables and estimated using Swedish data for 1989–2015. The results support three conclusions. First, the financial system is important and the strength of the results is dependent on identification, with the financial variables accounting for 10–25 % of the forecast error variance of Swedish GDP growth. Second, the suggested model produces an earlier signal regarding the probability of recession, compared to a model without financial variables. Third, the model’s forecasts for the deep downturn in 2008 and 2009, conditional on the development of the financial variables, outperform a macro-model that lacks financial variables. Furthermore, this improvement in modelling Swedish GDP growth during the financial crisis does not come at the expense of unconditional predictive power. Taken together, the results suggest that the proposed model presents an accessible possibility to analyse the macro-financial linkages and the GDP developments, especially during a financial crisis.
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Empir Econ (2017) 53:1553–1586
DOI 10.1007/s00181-016-1175-4
The importance of the financial system for the real
economy
Sebastian Ankargren1·Mårten Bjellerup2·
Hovick Shahnazarian3
Received: 19 February 2015 / Accepted: 12 August 2016 / Published online: 27 September 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract This paper first describes financial variables that have been constructed
to correspond to various channels in the transmission mechanism. Next, a Bayesian
VAR model for the macroeconomy, with priors on the steady states, is augmented with
these financial variables and estimated using Swedish data for 1989–2015. The results
support three conclusions. First, the financial system is important and the strength of
the results is dependent on identification, with the financial variables accounting for
10–25 % of the forecast error variance of Swedish GDP growth. Second, the suggested
model produces an earlier signal regarding the probability of recession, compared to a
model without financial variables. Third, the model’s forecasts for the deep downturn
in 2008 and 2009, conditional on the development of the financial variables, outper-
form a macro-model that lacks financial variables. Furthermore, this improvement
in modelling Swedish GDP growth during the financial crisis does not come at the
expense of unconditional predictive power. Taken together, the results suggest that
the proposed model presents an accessible possibility to analyse the macro-financial
linkages and the GDP developments, especially during a financial crisis.
Keywords Transmission channels ·Financial indicators ·Macroeconomy ·Business
cycle ·Credit cycle ·Bayesian VAR
JEL Classification E37 ·E44
BSebastian Ankargren
Sebastian.Ankargren@statistics.uu.se
1Department of Statistics, Uppsala University, Box 513, 751 20 Uppsala, Sweden
2Swedish National Debt Office, 103 74 Stockholm, Sweden
3Ministry of Finance, 103 33 Stockholm, Sweden
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1554 S. Ankargren et al.
1 Introduction
In late 2008, during the peak of the financial crisis, several markets at the heart of the
financial system more or less stopped functioning. Liquidity in the interbank market
dried up as banks refrained from lending money, even at short maturities. The price of
many assets fell quickly and deeply, central banks cut their policy rates drastically, and
the situation was characterized by great uncertainty. The crisis deepened the incipient
recession sharply, and most economists and policy makers were, at the time, surprised
by the dramatic effects on the real economy. But the close link between the financial
system and the real economy is not only there in times of crisis; it is also in place
under more normal economic circumstances. The effects are just less drastic.
In the wake of the financial crisis, researchers and policy makers have directed
much more attention to the linkage between the financial system and the real economy,
known as the transmission mechanism.1Great theoretical and empirical effort has been
devoted to better understand the way financial shocks affect the real economy (see,
for example, Bloom 2009;Bloom et al. 2012;Christiano et al. 2014). An important
part of the increased attention is that several governments are now considering more
far-reaching changes in supervisory and regulatory structures in order to protect the
real economy from the recurrent malfunctioning of the financial sector.2
However, if regulation is to be successful, at least two conditions have to be fulfilled.
First, policy makers need to have a qualitative knowledge of how the financial markets
affect the real economy through the transmission mechanism. Second, policy makers
need to have quantitative knowledge of the transmission mechanism in order to make
a balanced choice of the magnitude of a specific regulative measure.
Early efforts on modelling the transmission mechanism have largely focused on
modelling individual channels within the transmission mechanism, while later efforts,
not least within the dynamic stochastic general equilibrium (DSGE) literature, have
a richer modelling of the transmission mechanism.3It should be pointed out that
there are many studies that examine what effect individual transmission channels have
on the real economy. There are to our knowledge no empirical papers that estimate
the impact of all transmission channels.4The idea of this paper is to fill this gap by
suggesting a description of the transmission mechanism that fits into a small-scale
Bayesian VAR (BVAR) model. Despite being limited in terms of variables, the model
offers a number of possibilities in analysing various developments in financial markets.
The reason is that the financial variables used in the model are composites of several
financial variables, which in turn stem from the detailed underlying description of the
transmission mechanism. BVAR models resembling the one used in this paper have
1See, for instance, Basel Committee (2011).
2See, for instance, Basel Committee (2012a).
3See the survey from Basel Committee (2012b)andBrunnermeier et al. (2012). Jermann and Quadrini
(2012)andChristiano et al. (2014) are examples of two structural DSGE models that include financial
frictions in their models.
4However, there are general equilibrium models with a much more detailed modelling of the economy
that take account of several transmission channels at the same time. See, for example, Brunnermeier et al.
(2012).
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The importance of the financial system for the real economy 1555
been successfully used in related applied work (see, for example, Österholm 2010;
Jarocinski and Smets 2008 among others).
The model is essentially used to answer two questions: (i) Is the financial system
important for the development in the real economy? (ii) If it is, what are the charac-
teristics?
The paper has the following structure. In Sect. 2, we construct four composite
financial variables that act as indicators for the transmissions channels using data
from Sweden. In Sect. 3, the details of the statistical model of our choice, a BVAR
model with a prior on the steady state, are given. In Sect. 4, the model is used to answer
the two questions we have posed. Moreover, Sect. 4investigates the robustness of the
results by conducting a sensitivity analysis. Section 5concludes.
2 Choice and construction of the financial variables
The financial system mainly affects the real economy through the following transmis-
sion channels: the balance sheet channel, the interest rate channel, the bank capital
channel and the uncertainty channel. “Appendix” describes negative external effects
that cause the accumulation and liquidation of financial imbalances and the way the
identified negative externalities affect the real economy through the different trans-
mission channels. The purpose of this section is to describe the variables that we
use to quantify and isolate the impact of different transmissions channels on the real
economy.
2.1 Asset price gap and credit gap: the balance sheet channel
The balance sheet channel describes how falling asset prices affect households and
firms’ balance sheet, solvency and creditworthiness, and the impact that this may have
on consumption and investment. The prices of financial and real assets are important
for the solvency of firms and households. In order to capture the variations in both
financial and real asset prices, a summary index is therefore created based on how
the share and property prices change in relation to their historical trends. A gap is
generated for each of the stock and property markets as a first step towards creating
a summary variable. The stock price gap and the house price gap are defined as the
deviation of the stock market index and the property price index from their trend
divided by the trend.5In the second step, the share price gap (20%) and the property
price gap (80%) are combined in an asset price gap that summarizes the development
of house and stock prices.6
5The trend is calculated through a one-sided HP filter using a value of the weighting parameter equal to
400 000 (see, for example, Drehmann et al. 2010). The Basel Committee suggests using this method to
calculate the credit gap which is the deviation of the credit-to-GDP ratio from its long-term trend. The study
by Drehmann et al. (2010) indicates that the credit gap has been useful in signalling financial crises in the
past.
6Although the weighting is subjective, the aim has been to find weights that create a clear linkage between
the wealth gap and the GDP growth. The size of the weights also corresponds to the relative size of the
variance of the share price and property price gaps. We have also estimated the model with individual series
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1556 S. Ankargren et al.
Economic shocks lead to a fall in the value of borrowers’ assets at the same time as
the value of their loans is unchanged. In such cases, the borrowers’ balance sheets look
much worse than they had expected, with the result that they amortize part of their
liabilities (consolidate their balance sheets). So, asset prices and credit can reinforce
each other through the “financial accelerator effect”. To be able to control for this
mechanism, we include a credit gap to the set of variables. Technically, the credit gap
is constructed in the same way as the share price gap. As a first step the credit ratio is
generated (total lending in relation to GDP at current prices). The credit gap is defined
as the deviation of the credit ratio from its trend, divided by the trend.
2.2 The interest on three months treasury bills: the interest rate channel
The interest rate channel describes how the real economy is affected when the central
bank increases the repo rate. Monetary policy affects the interest on three months
treasury bills by changing the repo rate which is equivalent to changing the so-called
overnight rate.7
Monetary policy also affects the value of the currency (exchange rate channel).
Normally, an increase in the repo rate leads to a strengthening of the krona. A
stronger exchange rate—an appreciation—makes foreign goods cheaper compared
with domestically produced goods which lead to a rise in imports, a decline in exports
and a dampening of inflationary pressure. Moreover, a stronger krona tends to lower
the inflation rate, since imported goods and import-competing goods become cheaper.
This reinforces the dampening effect on inflation of falling demand. To be able to
control for exchange rate channel, we also include the real exchange rate.
2.3 The lending rate: the bank capital channel
The bank capital channel describes the impact that various risks have on bank balance
sheets and income statements and ultimately on their pricing behaviour and lending.
As in Karlsson et al. (2009), it is assumed that banks in Sweden operate on a market
characterized by monopolistic competition in which the banks’ lending rate is set as
a markup on their marginal costs.8The lending rate is therefore influenced by both
the interest rate that banks themselves have to pay to borrow funds and the interest
rate supplement added by banks when lending on to their customers. The study by
Karlsson et al. (2009) indicates that what the macroeconomic literature calls credit
spread contains many different components that are affected in one way or another by
Footnote 6 continued
for the house price gap and the share price gap and the results indicate that the conclusions of this paper
remain intact.
7A treasury bill is a short-term debt instrument issued by the Swedish National Debt Office. The maturity
of these instruments is usually up to one year. Treasury bills are used to finance the government’s short-term
borrowing requirement.
8For a more detailed description of the market structure and loan pricing equation, see Arregui et al. (2013),
Box 2.
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The importance of the financial system for the real economy 1557
various risks associated with bank operations. Using lending rates together with policy
rates in macroeconomic models makes it possible to analyse the effects these risks
have on the real economy, besides those stemming from monetary policy. The lending
rate that is used as an indicator is a combination of the interest rate that households
and firms actually pay on their existing loans.9
2.4 The financial stress index: the uncertainty channel
The uncertainty channel describes how more uncertainty in financial markets leads to
higher precautionary savings and lowers consumption. The degree of uncertainty on
financial markets is measured by a “stress index”. In line with the arguments presented
in Forss Sandahl et al. (2011) and Holmfeldt et al. (2009), an index is constructed that
is a composite of developments on four markets: the stock market, the currency market,
the money market and the bond market.10
2.4.1 Uncertainty in the stock and currency markets
There are several ways of measuring disturbances and uncertainty in the stock and
currency markets. One of the most important measures is volatility. There is a group of
volatility measures that are forward-looking since they are based on option prices (e.g.
VIX in the USA). But, from a practical econometric perspective, it is a disadvantage
that long time series are not available for these volatility measures. This is why this
paper uses the actual volatility in the OMX index, measured as the standard deviation
for the OMX index for the previous 30days. Moreover, the actual volatility of the SEK
exchange rate against the euro, measured as the standard deviation for the prices noted
for the previous 30 days, is used as a stress indicator for the currency market.
2.4.2 Uncertainty on the money market
In the money market, banks extend loans to one another at short maturities to manage
day-to-day liquidity needs. The interest rate on this interbank market is called LIBOR
(STIBOR in Sweden) and is often compared with the interest rate on treasury bills
with corresponding maturities. This interest rate difference, which is called the TED
spread, is used as a stress indicator for the money market.
2.4.3 Uncertainty in the bond market
The spread in interest rates between mortgage and government bonds, which is called
the mortgage spread, says something about how sellers assess the risks associated
with each bond. This makes this spread a good indicator of developments on these
9The lending rate is the weighted average of the interest rates for households (2/3) and the interest rate for
firms (1/3).
10 Johansson (2013) develop a more complex financial stress index and compare the development of this
new financial stress index with a simpler. See also Österholm (2010).
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1558 S. Ankargren et al.
two markets. The mortgage spread is therefore used as a stress indicator for the bond
market.
2.4.4 Financial stress index
The four indicators are first standardized so that they can be weighted to a composite
financial stress index. The weighting is done by giving each indicator the same weight.
The resulting index is also standardized which means that the summary financial stress
index has a mean of 0 and a standard deviation of 1 by construction, which facilitates
interpretation of the index. When the series has a value of zero, it is equal to its
historical mean and the stress level should therefore be considered normal. With this
standardization, a value of 1 also means that the level of stress is one standard deviation
higher than normal.
3 Methodology and data
In this section, we present the empirical model that we use to model the interaction
between the financial variables described in the previous section and the macroecon-
omy.
3.1 The empirical models
In a prototypical VAR model, the number of parameters is common to be large rela-
tive to the number of available observations. Inevitably, this leads to large estimation
uncertainty and a risk of overfitting. BVAR models, which combine the data with
the researcher’s prior beliefs, have proved to be successful tools as informative prior
distributions reduce estimation uncertainty while often generating only small biases
(Giannone et al. 2015). This approach is often also associated with improved fore-
casting performance (see, for example, Karlsson 2013 for a review), which makes the
BVAR an attractive choice and a standard tool in applied macroeconomics (Ba´nbura
et al. 2010).
The prior distribution in a Bayesian VAR model can be specified in a number of
different ways. In this paper, we use the steady-state prior (Villani 2009) as we believe
it offers a natural way of incorporating prior beliefs. Additionally, from a forecasting
perspective it is appealing to formulate the prior on the unconditional mean (the “steady
state”) directly, since for increasing forecast horizons the forecasts will converge to
the model’s estimate of the unconditional mean. Furthermore, the steady-state prior
has successfully been used in similar applied research in recent years, e.g. Jarocinski
and Smets (2008); Österholm (2010); Clark (2011).
The VAR model can be written on mean-adjusted form as:
Π(L)(xtΨdt)=εt,(1)
where Π(L)=(IΠ1L···ΠmLm)is a lag polynomial of order m,xtis an
n×1 vector of stationary variables, dtis a k×1 vector of deterministic variables, Ψ
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The importance of the financial system for the real economy 1559
is an n×kcoefficient matrix and εtis an n×1 vector of multivariate normal error
terms with mean zero and covariance matrix Σ. Note that Ψdtis the unconditional
mean of the process, meaning that priors on Ψreflect views about the steady states of
the variables.
The set of unknown parameters in the model in Eq. (1)isΘ={Ψ,Σ,Π1,...,Πm},
for which priors must be formulated. As suggested by Villani (2009), the unconditional
means in Ψare assumed to be independent and normally distributed. The specifics of
the prior distribution of Ψin our application are discussed in more detail in Sect. 3.4.
For the remaining parameters, Σand Π1,...,Πm, several different priors could in
principle be used since xtΨdtis a standard VAR given Ψ, for which a number of
alternatives exist. In this work, we follow common practice among previous studies
employing the steady-state prior and use a non-informative prior for the error covari-
ance matrix, p(Ψ)∝|Σ|n+1
2, and a Minnesota prior for the dynamic coefficients in
Π1,...,Πm.
The Minnesota prior (Litterman 1979) rests upon the stylized fact that many eco-
nomic variables behave approximately as random walks. Moreover, coefficients on
more distant lags as well as coefficients on variables in equations other than their
own are a priori believed to be less important than recent lags and lags of a vari-
able itself. These prior beliefs are incorporated through the use of hyperparameters,
which determine the degree of certainty of the beliefs. The dynamic coefficients in
Π1,...,Πmare a priori assumed to be independent and normally distributed with
means and standard deviations given by:
EΠpi,j=δi,if i=jand p=1,
0,otherwise (2)
VΠpi,j=
λ1
pλ3,if i=j,
λ1λ2si
pλ3sj
,otherwise (3)
Most commonly, the prior mean δiin Eq. (2)issettoδi=1 for all i. However, if
variable iis believed to be stationary or mean-reverting, δimay instead be set to, e.g.
0 or 0.9, as noted by Ba´nbura et al. (2010) and Karlsson (2013). Equation (3) contains
three hyperparameters: λ1>0 controls the overall tightness, 0
21 determines
the cross-equation shrinkage in the VAR and λ3>0 governs the lag decay. Larger
values of λ1will make the prior less informative by increasing the prior variances,
whereas smaller values will shrink the coefficients towards the prior means. For the
cross-equation tightness λ2, the upper bound means that lags of other variables in an
equation are not subject to additional shrinkage compared to own lags, while smaller
values will shrink the VAR model to a collection of AR models. Lastly, high values of
λ3will reduce the impact of higher-order lags, as opposed to low values which push
the model towards making no distinction between recent and distant lags. Together
with si, the residual standard deviation from univariate AR(4) models used to scale
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1560 S. Ankargren et al.
the coefficients, these hyperparameters parametrize the entire prior distribution for the
dynamic coefficients.
There are many alternative prior distributions that may be used and for surveys of
some of these we refer the reader to Karlsson (2013); Koop and Korobilis (2010). More
recently, advances have also been made on important topics such as variable selec-
tion and large BVARs. George et al. (2008) developed a stochastic search to selecting
restrictions in the BVAR, and Korobilis (2013) proposed an automatic variable selec-
tion approach, which Louzis (2015) subsequently applied in conjunction with the
steady-state prior. For models of larger dimension, Ba ´nbura et al. (2010) showed that
a standard BVAR with a Minnesota-style prior performs well if the overall tightness
hyperparameter λ1is set in relation to model size.
The hyperparameters parametrizing the Minnesota prior are set in accordance with
the literature. Previous studies (e.g. Adolfson et al. 2007;Villani 2009;Österholm
2010) have used an overall tightness of λ1=0.2, a cross-equation tightness of λ2=
0.5 and a lag decay parameter of λ3=1.11 As discussed by Villani (2009), the
random walk prior of δi=1 for all iis not consistent with the notion of a steady state.
Instead we set δi=0.9 for variables in levels and δi=0 for differenced or growth
variables. We investigate the sensitivity of the hyperparameters and the prior for Ψin
the sensitivity analysis in Sect. 4.5.
3.2 Data
Specification of the model is subject to a trade-off between including all possible
variables and, on the other hand, parsimony. Since the degrees of freedom are quickly
exhausted when the number of variables and lags increases, a relatively small number
of variables is advisable. Too few variables and lags, however, will create problems
with omitted variable bias.
Our approach is to start with a small VAR model containing the most important
macroeconomic variables. This type of model is often used to analyse different types of
shocks (see, for example, Sims 1992;Gerlach and Smets 1995 for early contributions).
The original models typically included three variables: a short-term interest rate, the
inflation rate and GDP growth. This paper expands on the three-variable model by
also including unemployment so as to be able to take account of developments on the
labour market. Since Sweden is a small, open economy, the currency rate and foreign
GDP growth are also included in the set of variables.12 Inflation is calculated based
on the core consumer price index (CPIX), which is CPI with households’ mortgage
costs and direct effects of indirect taxes excluded. Foreign GDP growth is calculated
as a KIX13 weighted GDP growth using the 16 most important countries for Sweden.
11 Despite the chosen values being standard choices, they are nonetheless arbitrary. An alternative is a
hierarchical approach, which is discussed in detail by Giannone et al. (2015). By integrating out the model
parameters, we can obtain the likelihood of the data given the hyperparameters, called the marginallikelihood
(ML). Maximization of the ML as a function of the hyperparameters is an empirical Bayes strategy with
good performance, as demonstrated by the authors.
12 The exchange rate is also included to handle the exchange rate channel.
13 KIX is a weighted effective exchange rate index calculated by the Riksbank.
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The importance of the financial system for the real economy 1561
Tab l e 1 Variables and steady-state priors
Short name Series Macro-model Financial model Steady-state prior intervalsa
1992Q4 and earlier 1993Q1 and later
GDPF KIXbweighted
foreign real
GDP growth
x x (0, 1) (0.25, 0.75)
SI Financial stress
index
x(3, 3) (1, 1)
GDP Swedish real
GDP growth
x x (0.25, 0.875) (0.5, 0.625)
CPIX Core consumer
price index
inflation
x x (0.375, 0.9375) (0.25, 0.5625)
U Unemployment
rate
x x (4, 6) (5.7, 6.7)
ITB Interest on three
month
treasury bills
x x (5.5, 8) (3, 4.5)
IL Lending rate x (6.5, 9) (4, 5.5)
AGAP Asset price gap x (4, 4) (2, 2)
CGAP Credit gap x (4, 4) (2, 2)
ER KIXb(first
difference)
xx (3.1, 3.1) (3, 3)
aThe steady-state priors are presented as 95 % prior probability intervals for the unconditional means. The
underlying distributions are independent normals
bKIX is a weighted effective exchange rate index calculated by the Riksbank
In a second step, we augment the six-variable macroeconomic model with the four
financial variables discussed in the previous section, which we assume summarize
the relevant information in the financial sector. For comparative purposes, we will
use the model both with and without the financial variables in what we will refer to
as the macro- and financial models, respectively. The full list of variables and which
variables are included in the models is given in Table 1. The data are quarterly, ranging
from 1989Q1 to 2015Q2, and figures of all variables are shown in Fig. 1. In addition
to the macroeconomic and financial variables, the models include a dummy variable
for the period 1989Q1–1992Q4 to control for the shift in the Swedish exchange rate
regime. In effect, the steady states (unconditional means) of the variables are allowed
to shift following 1992Q4.
3.3 Identification
Conditional expectations and variance decompositions both make use of orthogo-
nalization of shocks. The orthogonalization is achieved by means of a Cholesky
decomposition, which is equivalent to a recursive identification scheme. As a con-
sequence, the ordering of the variables will matter for the final results. Thus, we here
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1562 S. Ankargren et al.
1990 1994 1998 2002 2006 2010 2014
2
0
1990 1994 1998 2002 2006 2010 2014
0
2
4
1990 1994 1998 2002 2006 2010 2014
4
2
0
2
1990 1994 1998 2002 2006 2010 2014
1
0
1
2
3
1990 1994 1998 2002 2006 2010 2014
5
10
1990 1994 1998 2002 2006 2010 2014
0
5
10
15
1990 1994 1998 2002 2006 2010 2014
5
10
15
20
1990 1994 1998 2002 2006 2010 2014
0
20
1990 1994 1998 2002 2006 2010 2014
0
20
1990 1994 1998 2002 2006 2010 2014
5
0
5
10
Data
Posterior Median of Steady State
95 % Intervals of Steady State
ISFPD
GGDP CPIX
U ITB LR AGAP
CGAP ER
Fig. 1 Posterior medians of steady states
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The importance of the financial system for the real economy 1563
present some motivation for the selected order of variables. While sound, theoretical
arguments for the ordering of all of our ten variables are lacking, some guidance can
be found in the literature. Hubrich et al. (2013) place foreign variables first followed
by output, inflation and interest rates, which is standard practice following Christiano
et al. (1999). These macroeconomic variables are followed by financial variables.
Christiano et al. (1996) place unemployment after production and inflation but before
the federal funds rate. Similarly, Eichenbaum and Evans (1995) place the real exchange
rate subsequent to production, inflation and a foreign short-term interest rate spread.
Motivated by this, for the macroeconomic variables we select the internal ordering
(GDPF, GDP, CPIX, U, ITB, ER).14
For the financial variables, there is no clear guidance how to order them. Abild-
gren (2012), Adalid and Detken (2007) and Hubrich et al. (2013) all agree that the
macroeconomic variables, with the exception of the exchange rate, should precede the
financial variables. Abildgren (2012) place credit in between share and house prices;
in our case, share and house prices are lumped together in the asset price gap and
credit is included in the form of the credit gap. Adalid and Detken (2007) place prop-
erty and equity prices prior to private credit growth, which is only succeeded by the
real exchange rate. Moreover, Adalid and Detken (2007) argue that the exchange rate
should be placed last. Goodhart and Hofmann (2008) similarly put house prices before
credit and Hubrich et al. (2013) estimate all 120 combinations of orderings within their
five-variable financial block and conclude that the effect on the results is only minor.
This taken together leads us into placing AGAP and CGAP in between ITB and ER.
Holló et al. (2012) and Roye (2014) make the argument that output should come
after the financial stress index due to the delay in publication of data, which makes it
unreasonable to believe that the financial stress index would react contemporaneously
to GDP growth shocks. This argument is made in the context of monthly data, but
the issue still remains in quarterly data, albeit in alleviated form. Based on this, our
main results are based on an ordering in which SI is placed first among the domestic
variables. Finally, the lending rate is placed between ITB and AGAP in order for the
lending rate to react contemporaneously to shocks to ITB. This also allows for asset
prices and credit to react immediately to lending rate shocks.
To assess the sensitivity to the ordering, alternatives are considered in the sensitivity
analysis in Sect. 4.5 as follows: 1) all financial variables, which are regarded as fast
moving, are placed after slow-moving macro-variables, 2) all macro-variables are
placed after the financial variables and 3) some of the financial variables are placed
before the macro-variables and some after.
3.4 Steady-state intervals
The priors on the unconditional means, or steady states, are given in Table 1and are
presented as 95% probability intervals for the variable in question. These choices
are similar to the previous literature (see, for example, Österholm 2010;Adolfson
et al. 2007) and deviations therefrom are only cosmetic. The Swedish GDP growth is
14 See Table 1for a key to the abbreviations.
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1564 S. Ankargren et al.
assumed to have a steady-state value centred on 0.5625%, which corresponds to a year-
on-year (YoY) growth rate of 2.25 %. The foreign GDP growth is given a wider interval,
with its centre being 0.5%. The prior probability interval for inflation is centred on
a YoY rate of 1.625 %, which is justified by the Riksbank’s inflation target of 2%
implying a somewhat lower rate for the CPIX measure. The short-term interest rate
has a prior unconditional mean of 3.75 % and the lending rate is given prior intervals
which are identical to the short-term interest rates’, but one percentage point higher.
This roughly constitutes the historical spread between the two. The unemployment is
centred on 6.2% because of the fact that unemployment in Sweden has historically
been low but did increase during the global financial crises. The prior for the exchange
rate is centred on 0, with quite wide intervals.
For the stress index, we centre the prior on 0 seeing that this is the mean of the
series by construction. The credit gap and the asset price gap are both centred on 0%
as they are both gaps, which in the steady state should be closed. The upper bound is
set to 2%, which is motivated by the credit gap being a key indicator for the decision
to activate the countercyclical capital buffer and also to decide the level of this buffer.
According to the rules the counter cyclical capital buffer will be activated when the
credit gap is equal to or higher than 2 %. With this in mind, we therefore select 2% as
the upper limit of the prior interval for both credit gap and asset price gap.
As is evident from Fig. 1, there is a mixture of persistent as well as impersistent
variables in the model. Theoretically, the persistent variables should be mean-reverting
and not drifting off, which goes hand in hand with the idea of steady states. If these
variables were truly non-stationary, the steady state may not exist. In the simulation
of the posterior distribution, it is, however, possible to obtain draws which imply a
non-stationary VAR process, but we enforce the VAR to be stationary by discarding
these.
In order to accommodate a belief that the Swedish economy does not affect foreign
GDP growth, the latter is treated as block exogenous. This is achieved by using a
hyperparameter set to 0.001 that further shrinks the prior variances around zero for
parameters relating Swedish variables to the foreign GDP growth.
As a first glance at the results, in Figs. 1and 2the medians of the posterior steady-
state distributions and the within sample fits, respectively, are plotted. Figure 1shows
the importance of the dummy variable for some of the variables. In Fig. 2, a tendency
of better fit for the more persistent variables can be seen. However, it shows that overall
the within sample predictions follow the outcomes closely.
4 Empirical analysis
This section tries to answer two main questions: (i) Is the financial system important
for the development in the real economy? (ii) If it is, what are the characteristics?
Section 4.1 presents an evaluation of unconditional forecasts to gauge the general
forecasting ability. Section 4.2 addresses the question whether there is any interaction
by means of a forecast error variance decomposition. Sections 4.3 and 4.4 discuss
recession probabilities and conditional forecasts, respectively, in order to describe the
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The importance of the financial system for the real economy 1565
1990 1994 1998 2002 2006 2010 2014
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Within sample fit
ISFPDG GDP CPIX
UITB LR AGAP
CGAP ER
Fig. 2 Within sample fit
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1566 S. Ankargren et al.
characteristics of the interaction. Finally, a sensitivity analysis of the robustness of the
results is presented in Sect. 4.5.
4.1 Forecast evaluation
The pseudo-out-of-sample forecast evaluation is conducted with respect to the model’s
ability to forecast Swedish GDP growth.15 For the sake of comparison, we estimate
two main models: (i) a macro-model without the financial variables (abbreviated by
MAC) and (ii) the main financial model, i.e. the macro-model with four financial
variables (referred to as FIN). For a description of the methodology and variables
used, see Sect. 3. The evaluation also includes three simpler alternatives for reference.
The first is an AR(1) model, the second the most recent observation and the third the
mean over the most recent two years.
The out-of-sample forecast evaluation is carried out in the following way. The
period 2004Q2 to 2015Q2 is used as the evaluation period and the models make
recursive forecasts for these periods. This means that in a first step the models are
estimated using data from 1989Q1 to 2004Q1, after which forecasts are made for the
next one to eight quarters. In a second step, the models are re-estimated using data
from 1989Q1 to 2004Q2 and the forecasts are then redone with the same time horizons
as before.16 This then continues in the same way moving one quarter ahead at each
step. With this procedure each model has at most 45 forecasts (one quarter horizon)
down to 38 forecasts (eight quarter horizon), which make up the forecast series that
are evaluated. Each model’s forecast series is then compared with the actual outcome
series and the forecasting abilities of the models are summarized by the root mean
squared error (RMSE) with a lower value indicating better forecasting ability. We take
the AR(1) model as the main benchmark and divide all other models’ RMSE values by
the AR(1)’s to obtain RMSE values relative to the benchmark. Thus, in Fig. 3, values
below 1 indicate superior forecasting performance relative to the AR(1).
The results in Fig. 3show that, in terms of point predictions evaluated by RMSE,
there is no major difference between the smaller, basic macro-model and the larger
model with financial variables. The figure also includes the RMSE values of these two
models, when the forecasts are made conditional on foreign GDP growth. This is done
in order to rid the model of possibly predicting badly due to an inability to forecast
foreign GDP growth; because of the imposed block exogeneity, the equation for the
foreign GDP growth series is essentially just an AR(4).
The conditional forecasts are improved compared with their unconditional coun-
terparts, even beating the benchmark AR(1) over all eight quarters. The unconditional
forecasts have lower RMSE than the AR(1) for the first three quarters, but for the entire
evaluation period the differences are small. Thus, one may conclude that in terms of
15 Forecast evaluations with respect to the other variables are available from the authors upon request.
16 In the evaluation, the latest regular outcome figures for the national accounts have been used as the
outcome series for Swedish GDP growth. One reason why a real time series has not been used is that
the evaluation is not primarily intended to evaluate the absolute forecasting ability of the model including
financial variables, but its forecasting ability relative to a model that excludes such variables.
123
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The importance of the financial system for the real economy 1567
0123456789
0
0.5
1
1.5
2
Forecast horizon
Relative RMSE
FIN mod FIN mod, cond MAC mod
MAC mod, cond Recent mean No change
Fig. 3 Root mean squared errors (RMSE) for predictions of Swedish GDP growth relative to AR(1)
forecasting accuracy, there is in essence no difference between the macro-model and
the financially augmented model.17
The finding in this paper is partly in line with Espinoza et al. (2012). They use
different time series models to estimate the importance of financial variables for the
real economy and conclude that financial variables contribute to a better prediction of
euro area GDP, especially in 1999 and between 2001 and 2003, when using conditional
predictive ability tests and a rolling RMSE metric. However, when out-of-sample
forecasts are judged under a static RMSE metric, as is done here, they conclude that
financial variables do not help in forecasting real activity in the euro area. This is partly
because of the assumed identification, in which they isolate the responses of slow-
moving variables (the real ones) from that of the fast-moving variables (the financial
ones) so that the impulse response associated with shocks to financial variables can be
seen as marginal impacts beyond what is already accounted for by the real variables.
4.2 Variance decomposition—is there any interaction?
In order to answer our first question, if there is an interaction, we perform a forecast
error variance decomposition. Such a procedure estimates how much of the forecast
error variance of each of the variables can be explained by exogenous shocks to the
other variables. The decomposition is presented in Fig. 4. In order to more clearly
see what part can be attributed to the traditional macro-variables and to the financial
variables, the variables are combined into three groups: macro-variables, financial
variables and Swedish GDP growth. The decomposition of the forecast error variance
of Swedish GDP growth, with respect to these groups, is presented in Fig. 5.
17 The forecasts have also been evaluatedby mean absolute error and bias, which are available upon request.
The results and conclusion from these measures are in line with what is presented here. All models have a
minor negative bias, but the size, around 0.2 to 0.4, is similar in size to the AR(1) model’s. However,
due to the small sample, one or two larger misses have a quite large effect on the bias calculation.
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1568 S. Ankargren et al.
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GDP F shock SI shock GDP shock CPIX sho ck Ushock ITB shock LR shock AGAP shock CGAP shock ER shock
GDP FSIGDPCPIXUITBLRAGAPCGAPER
Fig. 4 Variance decomposition. Note Shocks have been orthogonalized using a Cholesky decomposition, in which the ordering is the order in which the variables appear in
the figure
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The importance of the financial system for the real economy 1569
0
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Financial variables
0
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Median 68 % 95 %
0 5 10 15 20 0 5 10 15 20 0 5 10 15 20
0
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1
Swedish GDP growth
Fig. 5 Median forecast error variance decomposition of GDP with respect to macroeconomic variables,
financial indicators and GDP itself
Figure 5shows that the financial variables are responsible for a non-negligible
part of the forecast error variance of Swedish GDP growth: in total, they account for
approximately 10 %. At around 6 %, the stress index accounts for the largest share of the
forecast error variance of Swedish GDP growth among the financial variables. Thus,
the results suggest that financial variables are important for the real economy, but not
immensely so. It shows that it can be important to account for many different aspects
of the transmission channels to get a deeper understanding of the macro-financial
linkages.
This finding is in line with, but smaller than, the findings of the structural DSGE
models such as Christiano et al. (2014) and Jermann and Quadrini (2012). Chris-
tiano et al. (2014) study US macroeconomic data over the period 1985–2010 and
conclude that the risk shock (the cross-sectional standard deviation of an idiosyncratic
productivity shock) accounts for a large share of the fluctuations in GDP and other
macroeconomic variables. Jermann and Quadrini (2012) find that financial shocks con-
tributed significantly to the observed dynamics of real and financial variables. More
specifically, they find that changes in credit conditions contributed to the downturns in
1990–1991 and 2001 as well as the 2008–2009 recession. Compared to Hubrich et al.
(2013), who find a combined effect of five financial shocks on different measures of
output to be around 5–45%, the proportion due to financial variables found here is
lower. Similarly, Adolfson et al. (2013) found, using a DSGE model for Sweden, that
the shock related to the financial block, which is the entrepreneurial wealth shock in
their model, explains about 3% of the variation in output growth.
As for the reverse relationship, rows 2, 7, 8 and 9 in the third column in Fig. 4
display the share of the forecast error variance of the respective row variables that can
be attributed to exogenous shocks to Swedish GDP growth. For the stress index, the
lending rate and the asset price gap, Swedish GDP growth appears to be of modest
importance. For the credit gap, however, the share attributable to Swedish GDP growth
shocks is around 10–15 %. Thus, it seems as if the interaction is two-sided. However,
it should be emphasized that the model has been constructed with a certain theoretical
setting in mind, namely the literature on negative financial externalities and the trans-
mission mechanism. The paper thus sets out to answer a one-sided question, namely
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1570 S. Ankargren et al.
how the financial system affects the real economy. Although the model seems to indi-
cate that Swedish GDP growth affects the credit gap, the model is not constructed to
analyse the determinants of the credit gap or any of the other financial variables for that
matter. It should of course not be ruled out that the model could be useful in this sense,
but any results in this respect should be interpreted with caution. In the sensitivity
analysis in Sect. 4.5, the alternative ordering produces a clear one-sided interaction,
which is more in line with the findings in the DSGE literature such as Rabanal and
Taheri Sanjani (2015). They show that financial frictions amplified economic fluc-
tuations and the measure of the output gap in all euro countries except France and
Germany. On the contrary, in France and Germany financial frictions played a minor
role in output gap measures. Financial frictions mattered much more precisely because
financial and housing demand shocks are the ones that set the financial accelerator in
motion, and those shocks were more important in all euro countries except France and
Germany.
To conclude this section, an interaction appears to exist as we find that financial
shocks are responsible for approximately 10 % of the forecast error variance decom-
position of Swedish GDP growth. The results indicate that a financial stress shock
is of particular importance to the real economy. In the opposite direction, a Swedish
GDP growth shock appears to be of more importance to the forecast error variance
of credit gap than to the forecast error variance of the other financial variables, with
an attributable proportion of around 15%. It should again be noted that the ordering
of the variables affects the result. The sensitivity analysis in Sect. 4.5 reveals that an
alternative ordering alters the conclusion somewhat, although the main conclusion
of the existence of a linkage remains intact. Indeed, it is in fact strengthened by the
alternative ordering.
4.3 Recession probabilities—can the interaction help us make better
predictions?
In this subsection, we study the models’ implied probabilities of recession. Österholm
(2012) performs an alternative forecasting exercise with a steady-state BVAR for the
USA. Standing in 2008Q2, the focus of the paper is the model’s estimated probability
of a coming crisis, with a crisis defined as two consecutive quarters of negative GDP
growth. We take the same approach and let the recession probability at time tbe
defined as the probability of negative Swedish GDP growth at both time t+1 and
t+2. The probability is estimated by computing the proportion of draws in the
numerical simulation of the model in which the unconditional forecasts of Swedish
GDP growth in t+1 and t+2 are negative. We calculate the probability over the time
period 2004Q1 and 2015Q1 for both the macro- and the financial models.18
Before commenting on the results in Fig. 6, it is important to stress that the exercise
should not be interpreted as intended to compete with the large literature on early
18 We have also generated the model’s predicted probability of a coming crisis between 2004Q1 and
2015Q1, with a crisis defined as one quarter of negative growth. The estimated probability is even better
when the model only needs to estimate one quarter. These estimates are available upon request.
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The importance of the financial system for the real economy 1571
2004 2006 2008 2010 2012 2014
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Year
Probability of recession
Negative growth Financial model
Macro model
Fig. 6 Future recession probabilities. Note A future recession is defined as negative growth in the next two
quarters
warning indicators.19 Furthermore, such indicators are usually economic or financial
variables or the product of a relatively simple model. Rather, the measure of recession
probability in this paper comes from a model trying to incorporate the transmission
mechanism within a macro-model. The probability measure is the product of macro-
and financial developments, interpreted in a reduced form model of the economy. Thus,
the relevant benchmark is whether or not the financial model is better at signalling
short-term recession risk than is the traditional macro-model.
Beginning in 2007 and ending right before the crisis unfolded in 2008Q3, both
models’ probabilities of recession steadily increase. The financial model shows a
more rapid increase, but up through 2008Q1, the two models’ estimated probabilities
are more or less the same. However, standing in 2008Q2—right before Sweden started
experiencing negative growth—the financial model’s probability of negative growth
in both 2008Q3 and 2008Q4 is 16 %, whereas the corresponding estimate from the
macro-model is 7%. One could argue that given the lag in response, this indicator
would not be an informative leading indicator. However, as mentioned earlier, this
indicator produced a clear signal one to two quarters before the global financial crisis
hit the Swedish economy.
Beginning in late 2010, the probability of a recession is again slowly increasing
following the events leading up to the intensification of the Eurozone crisis, with the
European Central Bank reactivating its Securities Markets Program in August 2011.
This is directly reflected in the financial model’s estimated recession probability, but
is at the same time barely visible in the macro-model’s because of its lack of financial
variables. This indicates that the more complete picture given by the inclusion of
financial variables is important, as it provides information about the developments in
the financial markets, which are likely to eventually transmit into the real sector.
19 A recent example is López-Salido et al. (2016).
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1572 S. Ankargren et al.
These findings are also in line with Fornari and Lemke (2010). The authors use a
probit regression model linked to a VAR model, a so-called ProbVAR model, to gen-
erate conditional recession probabilities and conclude that forecasts can be markedly
improved by adding financial variables to this model. It is encouraging that our results
agree with Fornari and Lemke (2010) in the sense that financial variables appear to
be highly useful and informative. One conclusion from the finding in this paper as
well as previous findings is that accounting for financial variables in the episode of
a financial crisis enhances model fit and statistical indicators such as probability of
recession, compared to the model that does not have a way to systematically account
for the financial variables.
One way to complete the recession probability analysis in this paper is to make a
careful analysis of the financial variables included in the model. López-Salido et al.
(2016) argue that elevated credit market sentiment at year t2 (proxied by the
predicted year-tchange in the credit spread) is associated with a decline in economic
activity in years tthrough t+2.20 That is, when their sentiment proxies indicate that
credit risk is aggressively priced, this tends to be followed by a subsequent widening
of credit spreads, and the timing of this widening is, in turn, closely tied to the onset
of a contraction in economic activity. One way to further develop the model in this
paper is to include the credit market sentiment in financial stress index instead of TED
spread and mortgage bond spread.21 This is left as a suggestion for future research.
4.4 Conditional forecasts—can the interaction improve our estimates of crisis
effects?
Another way of analysing whether there is any potential gain of including financial
variables in the model is by conditioning on them to investigate how the forecasts
change had we known their developments. In particular, it is illustrative to make fore-
casts before the crisis fully developed and compare the predictions from the financial
model, conditional upon the financial variables, with the macro-model’s predictions.
If the model is reasonably constructed, it should to some degree signal that times are
worsening if we condition upon the financial development during the crisis. The con-
ditional forecasts in this subsection are reminiscent of the conditional predictions in
Ba´nbura et al. (2015) and Espinoza et al. (2012). As also pointed out by these authors,
in the current setup conditional forecasts are created by shocking the system in such
a way that the conditioning path is satisfied, which imposes restrictions on the errors.
Contrary to Ba´nbura et al. (2015), shocks are produced in a structural representation
of the model and then transformed to the reduced form.22 For this reason, the ordering
20 The credit spread is measured by the spread between yields on seasoned long-term Baa-rated industrial
bond and yields on comparable-maturity Treasury securities.
21 Another way to do this is to divide the financial stress index to two different stress indices: one that
capture the volatility at the stock and currency markets and one that captures the spreads in the money and
bond market. In that case, the spread for the bond market can be replaced by the credit market sentiment.
22 The method is similar to that used by Adolfson et al. (2005). More specifically, the procedure at forecast
horizon his as follows: 1) shocks for the macroeconomic variables are drawn from their estimated distri-
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The importance of the financial system for the real economy 1573
2008 2009
4
2
0
2
Annual GDP growth
Outcome Forecast at 2007Q4 Forecast at 2008Q1 Forecast at 2008Q2
Fig. 7 Financial model’s forecasts of annual GDP growth in 2008 and 2009. Note The forecasts are made
conditional upon the financial indicators and foreign GDP growth during 2008 and 2009
of the variables will affect the results as the transformations are based on a recursive
identification scheme.
This hypothetical exercise is carried out as follows. The two models are estimated
using data up to and including 2007Q4. Both models make out-of-sample conditional
forecasts of Swedish GDP growth in 2008 and 2009 by transforming the quarterly
forecasts to annual growth. The financial model’s forecasts are conditional on the out-
comes of foreign GDP growth and the four financial variables in 2008Q1–2009Q4 and
the macro-model’s forecasts are conditional on the outcome of foreign GDP growth
throughout the same period. Next, data for 2008Q1 are added to the estimation sam-
ple and the out-of-sample conditional forecasting repeated (conditional on outcomes
in 2008Q2–2009Q4). Lastly, the same task is repeated a third time using also data
for 2008Q2 in the estimation (with forecasts conditional on outcomes in 2008Q3–
2009Q4). Thus, the exercise results in three forecasts for 2008 and three forecasts for
2009 for each model, with each forecast triple containing forecasts using increasing
amounts of information in the estimation.
Figures 7and 8display the financial and macro-models’ predictions for annual
growth in 2008 and 2009, respectively. As expected, the financial model provides
forecasts that are more in line with the outcomes. In Fig. 7, for the model estimated
at 2007Q4 the negative growth in 2008 is not anticipated, but a downturn in 2009
is predicted, although not the full size of it. Estimating the model at 2008Q1 and
2008Q2 gives more pessimistic forecasts that are closer to the outcome. This should be
contrasted to Fig. 8and the macro-model’s predictions. The pattern of more pessimistic
predictions as the estimation sample is increased persists, but they are markedly more
optimistic at around 1.5 % than the financial model’s at around 3%. A difference
of 1.5 percentage points might at a first glance seem small, but could be viewed in
light of the small differences in unconditional forecasts (see Table 2). Another way of
butions, 2) a shock to foreign GDP growth is injected such that it satisfies its condition and 3) given the
previous shocks, financial shocks are drawn to make the financial variables meet their conditions.
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1574 S. Ankargren et al.
2008 2009
4
2
0
2
Annual GDP growth
Outcome Forecast at 2007Q4 Forecast at 2008Q1 Forecast at 2008Q2
Fig. 8 Macro-model’s forecasts of annual GDP growth in 2008 and 2009. Note The forecasts are made
conditional upon foreign GDP growth during 2008 and 2009
describing the 1.5 % point difference is to relate it to the long-run average of Swedish
GDP growth, which stands at approximately 2% for the sample used in this paper
(1989Q1 to 2015Q2). Yet another is to relate the 1.5 percentage point difference to
the standard deviation of the series, which is 0.93. Summing up, the financial model’s
prediction of Swedish GDP growth in this exercise seems to be better compared to the
macro-model’s forecast.23
These forecasts illustrate what is perhaps the greatest advantage of augmenting
a traditional macro-model with financial variables. With the financial model, it is
possible to carry out forecasts that are conditional on developments in the financial
system, an exercise that is of particular interest when these exhibit increased volatility.
As discussed in the identification section (Sect. 3.3), these forecasts are dependent
upon the ordering of the variables. For this reason, in the sensitivity analysis (see
Sect. 4.5) we consider an alternative ordering and redo the preceding forecasting
exercise.
The conditional forecast exercise in this subsection is generally in line with the
findings in IMF (2015), Ba´nbura et al. (2015) and Espinoza et al. (2012). IMF (2015)
analyse how balance sheets of households and non-financial corporations affect the
economic outlook using a BVAR model. They find that using balance sheet informa-
tion improves the forecast of aggregate activity when applied to the USA. In addition,
shocks to balance sheets are also found to have a significant and differentiated eco-
nomic impact, suggesting that balance sheets carry additional information. Ba´nbura
et al. (2015) also use a BVAR model, as well as a large dynamic factor model, to
estimate the interaction between 26 euro area macroeconomic and financial variables.
They find that the two approaches generally deliver similar forecasts and scenario
23 While the median forecast may be close to the realization, it may be associated with large uncertainties.
For this reason, we have also compared prediction intervals from the models. The financial model’s predic-
tion intervals are generally wider, but only by 5–15%. The complete results are available from the authors
upon request.
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The importance of the financial system for the real economy 1575
assessments. Their conditional forecast exercise is similar to ours, but instead they
condition on real GDP, consumer prices and short-term interest rates. Interestingly,
the forecasts are generally in line with the outcomes, with the exception of loans to
households and firms where the major downturn is not fully anticipated. In our case,
the situation is the opposite since we condition upon the financial variables to make
improved forecasts of the growth of GDP. Espinoza et al. (2012) find similar, improved
results when forecasting ability is assessed as if in real time. In their base line model,
however, financial variables does not provide any extra information to their model
where euro area GDP growth is explained by the USA and Rest of the World GDP
growth. In short, the literature on conditional forecasts seems to find some evidence of
a two-way macro-financial linkage, although the amount of research still is too limited
to allow for any strong conclusions in this respect.
4.5 Sensitivity analysis
We investigate the robustness of the results by conducting a sensitivity analysis. This
is done in two parts. In the first part, the specifications in the prior are changed one
by one, changing the ordering of the variables and also considering the Minnesota
prior (i.e. without an informative prior on the steady state). We check the robustness
by studying how future forecasts and the forecast error variance decompositions after
20 periods change. In the second part, we select one alternative ordering and rerun the
analysis in full with the alternative ordering to investigate the sensitivity of the results
to our particular choice.
The first part includes changing the hyperparameters λ1
2and λ3, relocating the
centre of the prior probability intervals for the steady states and also extending the
width of the prior intervals for the steady states. All changes are done one by one.
Finally, we elaborate with alternative identification assumptions, where the financial
variables are added after the macro-variables, to see how important the identification
assumption is for the results.
The first two hyperparameters λ1and λ2are increased so as to relax the informa-
tiveness of the prior. The lag decay parameter, λ3, is also increased, but this has the
opposite effect of a tighter prior. For the steady-state priors, we consider two types
of changes. First, we let all prior probability intervals keep their length, but shift
their centre upwards by one standard deviation. Second, the prior probability intervals
remain centred on the same values, but the length of the intervals is doubled. These
two changes will give us some idea of how the results are affected by the location
and the tightness of the steady-state priors. We also investigate sensitivity with respect
to the ordering of the variables. Three alternative orderings are considered, in which
1) the financial stress index is moved to the second-to-last position, 2) most of the
financial variables come first and Swedish GDP growth is last and 3) asset price gap
and credit gap are moved to come prior to the stress index. By doing this, the hope
is to gain an understanding of how important the ordering actually is. The results are
displayed in Table 2.
Table 2suggests that with respect to the choices for the prior, the results seem to
be insensitive. The future forecasts and the forecast error variance decompositions
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1576 S. Ankargren et al.
Tab l e 2 Sensitivity analysis
Prior changed Forecasts of annual GDP growth FEVD of GDP, 20 periods
2015 2016 2017 2018 2019 Macro Financial GDP
Baseline 2.97 3.34 2.90 2.59 2.33 0.37 0.10 0.53
λ13.06 3.46 2.82 2.53 2.33 0.45 0.14 0.41
λ23.03 3.38 2.84 2.57 2.35 0.43 0.13 0.44
λ32.94 3.30 2.94 2.63 2.37 0.35 0.08 0.57
GDPF Mean 2.98 3.33 2.87 2.62 2.36 0.37 0.10 0.53
CGAP Mean 2.96 3.32 2.91 2.62 2.39 0.37 0.10 0.53
AGAP Mean 2.98 3.32 2.87 2.56 2.33 0.37 0.10 0.53
LR Mean 2.98 3.33 2.87 2.61 2.35 0.37 0.10 0.53
SI Mean 2.97 3.31 2.83 2.51 2.26 0.38 0.10 0.53
ITB Mean 2.98 3.30 2.84 2.56 2.32 0.37 0.10 0.53
ER Mean 2.99 3.37 2.92 2.61 2.33 0.37 0.10 0.53
CPIX Mean 2.97 3.36 2.90 2.61 2.35 0.37 0.10 0.53
UNEMP Mean 2.98 3.36 2.91 2.61 2.36 0.37 0.10 0.53
GDP Mean 2.97 3.33 2.88 2.58 2.33 0.37 0.10 0.53
GDPF SD 2.98 3.38 2.95 2.61 2.35 0.38 0.10 0.52
CGAP SD 3.01 3.41 2.87 2.56 2.32 0.38 0.10 0.53
AGAP SD 3.01 3.52 3.11 2.84 2.59 0.37 0.10 0.53
LR SD 2.97 3.37 2.93 2.61 2.34 0.37 0.10 0.53
SI SD 2.97 3.27 2.78 2.47 2.24 0.37 0.10 0.53
ITB SD 2.98 3.42 2.98 2.66 2.34 0.38 0.10 0.52
ER SD 2.99 3.38 2.92 2.61 2.35 0.37 0.10 0.53
CPIX SD 2.97 3.34 2.93 2.62 2.40 0.37 0.10 0.53
UNEMP SD 2.98 3.36 2.93 2.60 2.32 0.37 0.10 0.53
GDP SD 2.98 3.32 2.85 2.55 2.32 0.37 0.10 0.53
Order 1 2.98 3.35 2.87 2.61 2.36 0.39 0.07 0.54
Order 2 2.98 3.34 2.86 2.58 2.33 0.38 0.25 0.37
Order 3 2.98 3.37 2.88 2.57 2.30 0.36 0.22 0.42
Minnesota 2.90 3.00 2.65 2.56 2.55 0.36 0.10 0.54
The hyperparameters λ1
2and λ3are changed to 0.5, 1 and 2, respectively. The “Mean” row entires are
subject to a one standard deviation upward shift of the mean of the prior. The “SD” row entries are subject
to a doubling of the prior 95% probability interval. At the end of the table, the ordering of the variables is
reshuffled. Order 1 is (GDPF, GDP, CPIX, U, ITB, IL, AGAP, CGAP, SI, ER), Order 2 is (GDPF, CGAP,
AGAP, IL, ITB, SI, ER, CPIX, U, GDP) and Order 3 is (GDPF, AGAP, CGAP, SI, GDP, CPIX, U, ITB, IL,
ER). Lastly, the model is estimated with the Minnesota prior instead of the steady-state prior. The first block
of columns displays the annual forecasts of GDP growth of the coming years. The second block displays
the proportion of the forecast error variance decomposition of GDP 20 periods ahead, which is attributable
to the macroeconomic variables, the financial variables and Swedish GDP growth itself, respectively
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The importance of the financial system for the real economy 1577
are all similar when we change the hyperparameters and the centre and lengths of
the prior probability intervals. Using the alternative orderings does not affect the
future forecasts, as expected. When using the first alternative ordering of variables,
i.e. when the stress index is moved, also the forecast error variance decompositions
remain close to the main results. However, when considering the second or third
alternative ordering, the contribution to the forecast error variances which is due to
financial shocks increases from 10 to 22 and 25 %, respectively. By the nature of
the new orderings, this is hardly surprising; both of them place financial variables
(other than just the stress index) prior to Swedish GDP growth. As discussed in, for
example, Eichenbaum and Evans (1995), this means that we are implicitly assuming
that the preceding financial variables are causally prior to it. In our main ordering,
only the stress index is assumed to be causally prior to Swedish GDP growth. For
this reason, the second and third orderings illustrate the fact that as more financial
variables are assumed to be causally prior to it, the share of forecast error variances
attributable to these variables increases. Using the Minnesota prior leaves the forecast
error variance decompositions virtually unchanged, but the trajectory of the future
forecast is somewhat flatter.
In the second part of our sensitivity analysis, we redo the entire statistical analysis
with a new ordering. However, since most of the results are not dependent on the
ordering, only the parts which change, i.e. the conditional forecast exercise and the
forecast error variance decompositions, are presented here.
The alternative ordering considered is based on moving the asset price and credit
gaps from positions 8 and 9 to positions 2 and 3, i.e. placing them first among the
domestic variables. There are two main reasons for doing so. The results are most
sensitive to moving variables in the conditioning to positions before Swedish GDP
growth. Thus, if the results change, the induced change will likely be among the
larger changes that can occur by simply reordering the variables. Additionally, the
consequence of this reordering in a recursive setting is that the two gap series will
only react to all other domestic variables with a lag. Given their persistent nature, such
a slow reaction is sensible.
Since the internal ordering of the macro-variables is unchanged, the macro-model’s
forecasts are also unchanged and therefore not displayed here (instead, see Fig. 8).
The results of the conditional forecasts with the alternative ordering are displayed in
Fig. 9.
As seen in Fig. 9, the effect of changing the order of the variables is substantial.
The forecast of annual Swedish GDP growth in 2009 made in 2007Q4 is almost 2
percentage points lower than with the main ordering (see Fig. 7). Overall, the model
seems to be more responsive to the signals in the financial variables. This result has
a number of implications. First, it means that the ordering is a non-trivial task, which
affects the end result. Second, it implies that not only the choice of included variables is
important for the model’s performance, but also the (explicitly or implicitly) assumed
interactions, which hardly is surprising. However, it does not necessarily constitute a
problem. With a more elaborate and careful identification strategy, with identification
being a main focus rather than a necessary by-product of the analysis, one could
possibly obtain an even better performance of the model. This is also the case for the
steady-state priors. From time to time, structural changes may change the steady-state
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1578 S. Ankargren et al.
2008 2009
4
2
0
2
Annual GDP growth
Outcome Forecast at 2007Q4 Forecast at 2008Q1 Forecast at 2008Q2
Fig. 9 Financial model’s forecasts of annual GDP growth in 2008 and 2009. Note The forecasts are made
conditional upon the financial indicators and foreign GDP growth during 2008 and 2009, but with a different
ordering than in Fig. 4
assumptions given as priors in Table 1. With more elaborate and carefully chosen prior
beliefs, one could possibly obtain even better performance of the model because of the
structural changes.24 Furthermore, it would be highly interesting to incorporate non-
linearities in this context as well. It is plausible that the relationships are not constant
over time, as implicitly assumed here, but change. For example, if the conditioning
paths in some sense suggest crisis times, the model itself may need to change. To some
extent, this is achieved here by changing the ordering of the variables. This could then
suggest that different identifications may be useful at different times. Such nonlinear
macro-financial linkages are discussed in detail by Hubrich et al. (2013).25 However,
Fornari and Stracca (2013) provide some evidence of the non-linearities being absent
by estimating their model with and without crisis times and noting that the results are
fairly stable.
5 Conclusion and comments
The purpose of this paper has been to answer two main questions: (i) Is the financial
system important for the development in the real economy? (ii) If it is, what are the
characteristics? After estimating a BVAR model for the macroeconomy, augmented
with financial variables representing the transmission mechanism, a decomposition of
the forecast error variance of Swedish GDP growth reveals that the financial shocks
explain 10–25% of the variance, depending on the chosen identification. As might
be expected, an ordering where the financial variables come first generates the higher
forecast error variance.
24 Fornari and Stracca (2013), for example, consider sign restrictions as in Rubio-Ramírez et al. (2010).
A similar approach could be taken here.
25 In addition, such an identification pursuit and steady-state prior choices could also possibly mitigate the
relevance of the Lucas’ critique.
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The importance of the financial system for the real economy 1579
To see the characteristics of the financial-to-real interaction, two different
approaches were used. First, financial variables were included to see whether this
improved the model’s ability to estimate recession probabilities and second, condi-
tional forecasts were used to quantify the effects on Swedish GDP growth of a financial
crisis. The results indicate that the financial model, to a larger extent than the macroeco-
nomic model, acknowledges the increasing distress preceding the crisis. The financial
model’s estimated probability of a recession is stronger at an earlier date than the
macroeconomic model’s, thus connecting the financial turmoil to greater uncertainty
surrounding the growth of the economy. Second, and more importantly, the financial
model produces a more accurate forecast of the depth of the 2008–2009 crisis in Swe-
den, when comparing forecasts made in the quarters just before the outbreak of the
crisis. Conditional on the financial variables, the model’s forecast of Swedish GDP
growth over the coming two years is clearly lower—and thus more accurate—than
the macro-model’s forecast. In the alternative ordering of variables tried in the sen-
sitivity analysis, the advantage of conditioning on the financial variables is further
accentuated.
Furthermore, this increase in explanatory power does not come at the expense
of lower predictive power. There is no loss to point predictions, in terms of root
mean squared error (RMSE), by expanding a traditional macroeconomic model with
financial variables. Compared with the traditional macroeconomic model, there is no
difference as to their unconditional forecasting performances.
Concluding, the proposed macro-financial model fills a gap in the literature by
providing an empirical model that can be used to analyse and forecast the impact that
development of the financial sector may have on the real economy.
For future research, there are many interesting extensions one could make. The
inclusion of stochastic volatility in the steady-state prior, as proposed by Clark (2011),
appears to be a promising alternative. Similarly, allowing the model to have time-
varying or regime-switching parameters could be interesting ways of incorporating
non-linearities, which might improve the model even further.
Acknowledgements We would like to thank Thomas Bergman, Per Englund, Torbjörn Halldin and Martin
Solberger and the seminar participants at the Ministry of Finance and the Department of Statistics at Uppsala
University, three anonymous referees, and two editors for useful suggestions and discussions. A special
thanks to John Hassler, Pehr Wissén, Lars E O Svensson, Peter Englund, Mats Kinnwall, Lars Hörngren,
Johan Lyhagen, Fredrik Bystedt, Albin Kainelainen and Robert Boije for valuable comments on an earlier
draft of the paper and to Mattias Villani for allowing us to use his code. The views expressed in this paper
are solely the responsibility of the authors and should not necessarily be taken to reflect the views of the
Swedish National Debt Office and the Ministry of Finance.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
123
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1580 S. Ankargren et al.
Appendix: The theoretical effects of the financial system on the real
economy
We start by giving a detailed description of the way negative external effects cause
the accumulation and liquidation of financial imbalances. Next, we present a com-
prehensive but tractable description of the transmission mechanism and its channels.
Finally, we describe the way the identified negative externalities affect the real econ-
omy through the different transmission channels
Negative externalities and financial imbalances
The importance of the financial shocks and financial frictions is well summarized in
Brunnermeier et al. (2012). They point out that external funding is typically more
expensive than internal funding through retained earnings. This implies that agents
to a large extent issue claims in the form of debt which comes with some severe
drawbacks: an adverse shock wipes out large fraction of the levered borrowers net
worth, limiting his risk bearing capacity in the future. Hence, a temporary adverse
shock is very persistent since it can take a long time until agents can rebuild their net
worth through retained earnings. Besides persistence, an initial shock is amplified if
agents are forced to resell their capital. Since resales depress the price of capital, the
net worth of agents suffers even further (loss spiral). In addition, margins and haircuts
might rise (loan-to-value ratios might fall) forcing agents to lower their leverage ratio
(margin spiral).
The persistence of a temporary shock lowers future asset prices, which in turn feed
back to lower contemporaneous asset prices, eroding productive agents’ net worth
even further and leading to more resales which can lead to rich volatility dynamics
and explain the inherent stability of the financial system. Because of these mechanisms,
credit risk can be dwarfed by liquidity risk and cause a large discontinuous drop in
the price level and a dry-up of funding.
The role of financial institutions is to mitigate some of these financial frictions.
However, by investing in long-term projects and by issuing short-term debt claims,
financial institutions expose themselves to a liquidity mismatch, which can result in
fragility. Banks can also ensure that the borrower of funds exerts enough effort such
that loans can be repaid. Moreover, Banks can also create securities and offer deposit
contracts. However, by doing so, they expose themselves to bank runs. Financial
intermediaries are thus key in understanding the interaction between financial system
and real economy. Brunnermeier et al. (2012), Shin (2010) and Hanson et al. (2011),
among others, argue that financial institutions do not take adequate account of the
contagion effects of their behaviour on the financial system and the real economy.
Predatory pricing of risk, herd behaviour and the moral hazard in combination with
the implicit safety net may over time lead to financial imbalances. A correction of these
imbalances propagates rapidly through the financial system because of the linkages that
exist between the various financial institutions. Fire sale is another negative externality
that leads to asset prices fall even more and affect other institutions with similar
assets (e.g. Kashyap et al. 2011). In addition, financial institutions have much more
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The importance of the financial system for the real economy 1581
Credit/GDP
Time
Phase 1 Phase 2 Phase 3
Asset price and leverage spiral
Interconnectedness
Fire sale
Credit crunch
Balance sheet consolidation
Strategic complementarities
Pure informational
contagion
Agents: Banks, households and compnies Banks Households and companies
Fig. 10 Most important negative external effects in a stylized credit cycle
difficulties to roll over their short-term interbank funding (Perotti and Suarez 2009).
What matters in the end is perhaps not the boom or the asset price bubble in itself,
but rather whether the boom is financed by credit from leveraged institutions (Crowe
et al. 2011).
For the purpose in this paper, we use the framing of externalities presented and
discussed in De Nicolò et al. (2012). The negative externalities are illustrated in Fig. 10
in a stylized credit cycle.26
In phase 1 of the credit cycle, GDP growth is usually high and the situation is
characterized by rapidly rising credit volumes and asset prices, for both real and
financial assets. This phase involves banks, households and firms taking on increasing
amounts of debt, an increase in the risk associated with over-borrowing and in increase
in risk correlation. Phase 2 originates when a financial crisis is triggered. On the back
of falling asset prices, the banks experience capital adequacy problems, which must be
remedied. Phase 2 means that banks liquidate large parts of the financial imbalances
that were built up during phase 1. In phase 3, the mechanisms behind the problem are
very similar to those driving the banks in phase 2, i.e. balance sheet restructuring by
firms and households. It should be emphasized that the above description is schematic.
Phase 2 need not be preceded by phase 1, and phase 3 need not be preceded by phases
1–2, which constitute another argument for a broad approach.
26 The choice of three phases is made mainly on pedagogical grounds and should not be seen as in contrast
or as an alternative to earlier corner stones in the literature, e.g. Minsky (1992)andKindleberger (2000).
Footnote 26 continued
Thus, this paper’s naming and numbering of the phases is not the main issue, rather the stylized credit cycle
should be viewed as a link between the negative externalities and the transmission channels.
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1582 S. Ankargren et al.
The transmission channels
The financial system mainly affects the real economy through the following transmis-
sion channels: the balance sheet channel, the interest rate channel, the bank capital
channel and the uncertainty channel.27
The balance sheet channel describes how falling asset prices, e.g. house prices
and stock prices, reduce the value of assets held by households and businesses.28
If the value of assets used as security for loans falls, lenders can tighten collateral
requirements at the same time as the loan terms get worse, e.g. because the lending
rate is higher or because the borrower is not allowed to borrow as much as they may
want to. Next, this weighs even further on asset prices, leading to a financial accelerator
effect and ultimately to a slowdown in consumption and investment.
The interest rate channel describes how the real economy is affected when market
interest rates rise, for example because the central bank increases the repo rate. Rising
market interest rates lead to higher financing costs for banks and consequently to
higher lending rates.29 This, in turn, leads to a slowdown in consumption/investments
by households/businesses.30 can have a negative effect on the price of financial and
real assets because the present value of future returns from these assets decreases.
Consumption also decreases as a result of this wealth effect.
The bank capital channel describes how different types of risk associated with the
operations of banks (e.g. market risk, credit risk and financing risk) worsen bank
balance sheets by, for example, reducing the value of the bank’s assets and equity.31
However, banks must meet certain requirements concerning capital adequacy, solvency
and liquidity. Banks can choose to either raise their lending rates (thereby increasing
their profit and equity) and/or reduce their lending to be able to fulfil these requirements
which lead to lower consumption and less investment.
The uncertainty channel describes how more uncertainty in the form of price fluctu-
ations in financial markets, i.e. greater volatility, leads to higher precautionary savings
and lowers consumption as well as less investment.32
27 The interested reader is referred to Basel Committee (2011,2012a,b); Antony and Broer (2010); Ministry
of Finance (2013); Gerkeetal.(2012) for summaries of the latest research in this area.
28 Along with the interest rate channel, it is also called the credit channel. See, for example, Bernanke and
Gertler (1989); Bernanke et al. (1998); Kiyotaki and Moore (1997); Holmström and Tirole (1997).
29 In principle, the policy rate is assumed to have an immediate effect on short-term market rates and lagged
effect on long-term rates.
30 Monetary policy can also affect the economy via the “exchange rate channel”. An increase in the policy
interest rate normally leads to exchange rate appreciation. A stronger exchange rate means lower import
prices, with the result that some domestic demand moves from domestic to imported goods. This moderates
the inflationary pressure and also leads to a weaker balance of trade. Moreover, monetary policy also affects
the economy through other channels via expectations.
31 See, for example, Stein (1998)andHeuvel (2002,2004).
32 See, for example, Antony and Broer (2010).
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The importance of the financial system for the real economy 1583
Weaker balance sheets
Loans more expensive
Weaker balance sheets and worsening cash flow
(Households and companies)
Weaker consumption/investment
Falling asset prices,
rising interest rates
BANKS
FINANCIAL
MARKETS
REAL
ECONOMY
Increasing
market, liquidity
and credit risk
BORROWERS
Higher
volatility
A
A
B
C
D
A
A
B
B
B
B
C
C
D
D
Balance sheet channel
Uncertainty channel
Interest rate channel
Bank capital channel
C
Fig. 11 Transmission mechanism flow chart, including the three main areas of the credit cycle’s negative
external effects
The linkages between negative externalities and transmission channels
The negative external effects can also be described in terms of their effect on three main
areas of the economy: (i) market liquidity, (ii) the supply of credit and (iii) the demand
for credit. Depending on the channel, the effects go via the financial markets, the banks
and/or households and firms before finally affecting the real economy. Dividing the
negative externalities into these three main areas allows us to illustrate how they affect
the real economy through various transmission channels as illustrated in Fig. 11.The
different transmission channels in the economy have been marked by letters A to D in
the figure; (A) the interest rate channel; (B) the bank capital channel; (C) the balance
sheet channel; and (D) the uncertainty channel.33
Information contagion and fire sales are typical externalities affecting market liq-
uidity during phase 2, characterized by major volatility and rapid general declines in
33 Figure 8is schematic and should not be interpreted as saying that the four transmission channels are
independent of one another.
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1584 S. Ankargren et al.
financial markets. Heightened volatility leads to increased uncertainty and is followed
by greater caution, thus curbing demand in the economy (the uncertainty channel).
The effects on the supply of credit occur in both phase 1 and phase 2, and intensify
the upturn or downturn. In phase 2, a higher degree of interconnectedness between
financial institutions intensifies the shocks, which exacerbates the credit contraction
or results in higher lending rates. This, in turn, leads to a fall in demand in the real
economy (bank capital channel).
The effects on the demand of credit occur in both phase 1 and phase 3 but is
perhaps most tangible in phase 3 when households and firms react to the decline in
asset prices. In order to reduce their debt ratio, households increase their precautionary
saving, which reduces aggregated demand in the economy (the balance sheet channel).
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DISCLAIMER: This Staff Discussion Note represents the views of the authors and does not necessarily represent IMF views or IMF policy. The views expressed herein should be attributed to the authors and not to the IMF, its Executive Board, or its management. Staff Discussion Notes are published to elicit comments and to further debate.
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