Global growth on life support? : the contributions of fiscal and monetary policy since the global financial crisis.
Date: 2019-04-23
Institutions: European Central Bank
Subjects: econometrics, fiscal policy, monetary crisis, monetary policy

Ursel Baumann, David Lodge, Mirela S. Miescu 

Working Paper Series 

Global growth on life support?  
The contributions of fiscal and 
monetary policy since the global 
financial crisis 

No 2248 / March 2019 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract

This paper compares the role of monetary and (cid:28)scal policy shocks in advanced

and emerging economies. Using a model with a hierarchical structure we capture the

variability of GDP response to policy shocks both between and within the groups

of advanced and emerging countries. Our results provide evidence that (cid:28)scal pol-

icy e(cid:27)ects are heterogeneous across countries, with higher multipliers in advanced

economies compared to emerging markets, while monetary policy is found to have

more homogeneous e(cid:27)ects on GDP. We then quantify the policy contribution on

GDP growth in the last decade by means of a structural counterfactual analysis

based on conditional forecasts. We (cid:28)nd that global GDP growth bene(cid:28)ted from

substantial policy support during the global (cid:28)nancial crisis but policy tightening

thereafter, particularly (cid:28)scal consolidation, acted as a signi(cid:28)cant drag on the sub-

sequent global recovery.

In addition we show that the role of policy has di(cid:27)ered

across countries. Speci(cid:28)cally, in advanced economies, highly accommodative mon-

etary policy has been counteracted by strong (cid:28)scal consolidation. By contrast, in

emerging economies, monetary policy has been less accommodative since the global

recession.

JEL Classi(cid:28)cation: C32, E42, E52
Keywords: (cid:28)scal policy, monetary policy, panel VAR, conditional forecast.

ECB Working Paper Series No 2248 / March 20191Non-technical summary

Strong policy support was necessary to reignite the economic recovery from the 2008

global (cid:28)nancial crisis. In part re(cid:29)ecting di(cid:27)erent economic conditions and challenges, the

policy response across advanced and emerging market economies (EMEs) was somewhat

heterogeneous. Up to now, very little attention has been paid to analyzing the di(cid:27)erences

(or similarities) in the role played by policy support in advanced economies and EMEs.

A deeper understanding of how policy contributed across both groups of countries would

bene(cid:28)t both policymakers and academics.

Our paper provides a contribution to this end by analyzing the combined role of

(cid:28)scal and monetary policies in shaping global growth since the global (cid:28)nancial crisis.

We estimate panel VARs at quarterly frequency with Bayesian methods, using so-called

hierarchical priors for two di(cid:27)erent groups (advanced economies and EMEs), to evaluate

and compare the e(cid:27)ect of policy on real GDP growth. The group of advanced economies

includes the United States, euro area, United Kingdom and Japan and covers the period

1998 to 2016, while the EME group is composed of Brazil, China, India and Russia,

covering the period 2000 to 2016. To discern the e(cid:27)ect of (cid:28)scal and monetary policy on

GDP growth, we use counterfactual scenarios in a structural setting. In addition, we

study the interaction and interdependency of the two branches of macroeconomic policy

over the past decade.

Our main (cid:28)ndings are summarized as follows. Consistent with previous studies we

(cid:28)nd that (cid:28)scal multipliers are mostly higher in advanced economies compared to EMEs.

Meanwhile, activity in emerging economies is a(cid:27)ected somewhat more strongly than in

advanced economies following a contractionary monetary shock. In addition, the response

of GDP to (cid:28)scal shocks is more heterogeneous (between and within groups) than the

response of GDP to monetary policy shocks.

Turning to the conditional scenarios, our results suggest that GDP growth in our

sample of countries bene(cid:28)ted from substantial policy support during the global (cid:28)nancial

crisis. But policy tightened thereafter as (cid:28)scal consolidation in advanced economies acted

ECB Working Paper Series No 2248 / March 20192as a signi(cid:28)cant drag on the subsequent global recovery. Moreover, the role of policy has

di(cid:27)ered across countries. Speci(cid:28)cally, in advanced economies, highly accommodative

monetary policy has been counteracted by strong (cid:28)scal consolidation.

By contrast, in EMEs, monetary policy has been less accommodative since the global

recession. From 2016, global policies have become more supportive overall, consistent

with the observed improvement in global activity in that period. However, the reliance of

the global recovery on policy support underscores the need for a gradual and calibrated

withdrawal of policy accommodation.

Finally, our results emphasize an important interdependence between monetary and

(cid:28)scal policies. Counterfactual scenarios undertaken for the United States suggest that

without the (cid:28)scal policy reaction, monetary policy would have needed to be signi(cid:28)cantly

more accommodative during the (cid:28)nancial crisis. Thereafter, however, (cid:28)scal consolidation

has required monetary policy accommodation for longer.

Indeed, without the (cid:28)scal

consolidation undertaken after 2011, interest rates could have risen above the zero lower

bound already in 2013. This also implies that without monetary policy support the

strong (cid:28)scal consolidation would not have been possible without causing a signi(cid:28)cant

slowdown in US growth.

ECB Working Paper Series No 2248 / March 201931

Introduction

In the aftermath of the 2008 global (cid:28)nancial crisis, the design of an e(cid:27)ective policy re-

sponse became the main priority around the world. Spurred by policy commitments

at the 2009 G20 London Summit, central bank and government interventions addressed

macroeconomic instability and slumping demand with substantial policy support includ-

ing (standard and non-standard) monetary policies and (cid:28)scal stimulus (Figure 1). Since

then, debate has raged over the e(cid:30)cacy, e(cid:30)ciency and appropriateness of the response.

To name but a few, the topics have covered the role of the mix between (cid:28)scal and mon-

etary policies (Krugman, 2015); the bene(cid:28)ts of unconventional monetary policies (Borio

and Zabai, 2016); and the long-term consequences of the policy response (IMF, 2017a).

In recent years, however, priorities have gradually changed. Growth in global activity

has revived over the past two years as the cyclical upswing gathers strength (IMF, 2017b).

Spare capacity across many economies has narrowed substantially and policymakers have

turned their attention towards policy normalisation. Albeit gradually, the ‘long decade’

of policy accommodation is apparently drawing to a close. Yet, as policymakers edge at

di(cid:27)erent speeds towards the stages of policy withdrawal, it is crucial for them and us to

understand the extent to which the global economy is still dependent on policy support.

Too quick a withdrawal could force the economy into a sharp reversal; too slow could

store up future troubles. This paper aims to understand the role of policies in supporting

activity over the past decade.

Up to now, very little attention has been paid to analysing the di(cid:27)erences (or simi-

larities) in the role played by policy support in advanced and emerging economies. This

is an important topic for research and policymakers. The global (cid:28)nancial crisis a(cid:27)ected

advanced and emerging economies di(cid:27)erently and required tailored policy responses. Fac-

ing a severe turmoil in their (cid:28)nancial markets many advanced economies confronted a

deep and long-lasting slowdown in activity. Some faced the challenges of operating mon-

etary policy at the zero lower bound; in subsequent years, others were confronted with

market-driven or politically necessitated (cid:28)scal consolidation. By contrast, partly be-

ECB Working Paper Series No 2248 / March 20194Figure 1: Policy intervention. Stylized facts.

(a) Key policy interest rates (percent)

(b) Cyclically adjusted primary balances (per-
cent of GDP)

cause emerging market economies rebounded more quickly in the immediate aftermath

of the global recession, the policy response di(cid:27)ered (ECB, 2016). A deeper understand-

ing of how policy contributed across both groups would bene(cid:28)t both policymakers and

academics.

This paper aims to contribute to this growing area of research by exploring the

heterogeneity in policy e(cid:27)ects across groups of countries. Using structural panel VARs

in the spirit of Jarocinski (2010) we examine the joint role of (cid:28)scal and monetary policies

in shaping global growth since the global (cid:28)nancial crisis in both advanced and emerging

economies. As Caldara and Kamps (2008) note, vector autoregressive (VAR) models

have become a key econometric tool to assess the e(cid:27)ects of monetary and (cid:28)scal policy

shocks. Our paper is therefore related to the wide literature on the identi(cid:28)cation of

monetary and (cid:28)scal policy shocks, which are well summarized in Ramey (2016).

The contribution of our work to the literature is fourfold. First, we estimate and

compare the e(cid:27)ects of policy across a range of advanced and emerging market economies

(EMEs). We estimate structural panel VARs for a set of large advanced and emerging

economies (US, euro area, UK, Japan, Brazil, China, India and Russia), which together

represent over half of global GDP (at purchasing power parity).

ECB Working Paper Series No 2248 / March 20195While the literature on the e(cid:27)ects of either monetary or (cid:28)scal policy in individual

countries is vast (see Ramey, 2016), fewer papers have provided comparisons of policy

transmission across countries. For monetary policy, Jarocinski (2010) compares the re-

sponses of monetary policy shocks in the east and west of Europe, while Mandler et

al. (2016) examine the heterogeneity across countries within the European Monetary

Union. There has been more limited investigation into the e(cid:27)ects of monetary policy in

EMEs. Mallik and Sousa (2012) analyze responses in large emerging markets. Perez-

Forero (2015) compares the transmission of monetary policy shocks in Latin America

using a hierarchical panel VAR. On the (cid:28)scal side, Burriel et al. (2009) compare the

responses of the United States and euro area to (cid:28)scal shocks. Ilzetzki et al. (2013) (cid:28)nd

that the output e(cid:27)ect of an increase in government consumption is larger in industrial

than in developing countries.

Second, we look at e(cid:27)ects of (cid:28)scal and monetary policy in combination. Particularly

for studies using VARs, the literature has tended to examine (cid:28)scal and monetary policies

in isolation. The Christiano, Eichenbaum and Evans (1999), Handbook of Macroeco-

nomics chapter, for example, concentrates on the identi(cid:28)cation of monetary policy shocks,

while Ramey (2016) devotes separate sub-sections to the topics of (cid:28)scal and monetary

shock identi(cid:28)cation. Other leading types of externally identi(cid:28)ed monetary policy shocks

such as the Romer and Romer (2004) narrative method, or the high frequency identi(cid:28)ca-

tion of Gertler and Karadi (2015) also focus narrowly on the question of understanding

monetary policy e(cid:27)ects. The picture is similar for (cid:28)scal policy: for example, Blanchard

and Perotti (2002) focus only on the role of government spending and tax shocks. In

examining the role of (cid:28)scal shocks in the United States, Caldara and Kamps (2008) in-

clude interest rate variables within the VAR speci(cid:28)cation but report only the economic

responses to government spending and tax shocks under a variety of identi(cid:28)cation ap-

proaches. The policy response to the global crisis required monetary and (cid:28)scal action

in an e(cid:27)ort to boost the demand. Separating monetary and (cid:28)scal policy overlooks the

potential policy interactions. Our study aims to understand the combined role of both

forms of policy.

ECB Working Paper Series No 2248 / March 20196Third, to discern the e(cid:27)ect of (cid:28)scal and monetary policy on GDP growth we use

counterfactual scenarios in a structural setting. We compare model forecasts conditioned

on actual policy developments with forecasts conditioned on a counterfactual policy path.

We judge the impact of policy on activity by assessing the di(cid:27)erence in projected paths for

GDP growth in the two scenarios across our sample of countries. In e(cid:27)ect, we ask: what

would have happened to the economy without the observed policy easing? Kapetanios

et al. (2012) and Lenza et al. (2010) conduct similar exercises in examining the role of

monetary policy in the UK and the US, euro area and UK respectively. However, both

approaches rely on the reduced-form model to inform the conditional scenarios used.

Our counterfactual exercise takes a di(cid:27)erent approach by relying on the structural form

of the model, attributing outcomes for policy speci(cid:28)cally to the relevant monetary and

(cid:28)scal shocks identi(cid:28)ed in our model. A particular advantage of our approach, in using

structural conditional forecasts, is that it captures the variability in the GDP response

to shocks (through identi(cid:28)cation of shocks).

Finally, we study the interaction and interdependency of the two branches of macroe-

conomic policy over the past decade. A number of recent papers (Bianchi and Ilut, 2017;

Bianchi and Melosi, 2017; Corsetti et al, 2016; Jaroci«ski and Ma¢kowiak, 2018) empha-

size the relevance of analyzing the policy mix for economic outcomes. Our contribution

is to use counterfactual scenarios to understand the role of di(cid:27)erent policies in the recent

period, asking the questions: how might monetary policy have behaved if (cid:28)scal policy

had been conducted di(cid:27)erently?; and how strong would (cid:28)scal support have needed to

be, had monetary policy been less accommodative? In asking these questions we aim to

provide an understanding of the interdependencies of policies and the e(cid:27)ect on activity

over the past decade.

In analysing monetary and (cid:28)scal transmission across countries, we employ an econo-

metric technique that allows us to analyze countries jointly within panel models. We

estimate separate models for the two groups of advanced and emerging economies and,

following Jarocinski (2010), use Bayesian estimation. The approach employs so-called

hierarchical priors which have the assumption that parameters are drawn from a common

ECB Working Paper Series No 2248 / March 20197mean across each group, but allow for heterogeneity in the coe(cid:30)cients via the hierarchical

prior which is endogenously determined and governs the degree of heterogeneity across

individuals. In doing so, it makes e(cid:30)cient use of available data. In particular, for EMEs

for which time series are relatively short, this is a considerable advantage. A further ben-

e(cid:28)t is that the approach can reveal heterogeneity in the propagation mechanism within

each group and also between di(cid:27)erent policy tools.

Our main results can be summarised thus. Consistent with previous studies we (cid:28)nd

that (cid:28)scal multipliers are mostly higher in advanced economies compared to EMEs. We

also show that in the last two decades AEs have conducted an active monetary policy

which has tended to o(cid:27)set the e(cid:27)ects of expansionary government spending measures. In

contrast we see less strong reaction of monetary policy to (cid:28)scal policy shocks in EMEs.

The response of GDP in EMEs to monetary policy shocks is also broadly in line with the

literature, with activity in emerging economies a(cid:27)ected somewhat more strongly than in

advanced economies following a contractionary monetary shock. Moreover, we (cid:28)nd that

the GDP response to (cid:28)scal shocks is more heterogeneous (between and within groups)

than the response of GDP to monetary policy shocks.

Turning to the conditional scenarios, we (cid:28)nd that GDP growth in our sample of coun-

tries bene(cid:28)ted from substantial policy support during the global (cid:28)nancial crisis but policy

tightened thereafter, particularly as (cid:28)scal consolidation in advanced economies acted as

a signi(cid:28)cant drag on the subsequent global recovery. Since 2016, policies have become

more supportive overall. That is consistent with the observed improvement in global ac-

tivity in that period although it also emphasizes that the global recovery has been reliant

on policy support and underscores the need for a gradual and calibrated withdrawal of

policy accommodation. In addition we show that the role of policy has di(cid:27)ered across

countries. Speci(cid:28)cally, in advanced economies, highly accommodative monetary policy

has been counteracted by strong (cid:28)scal consolidation since 2011. By contrast, in EMEs,

monetary policy has been less accommodative since the global recession.

Finally, our results emphasise an important interdependence between monetary and

(cid:28)scal policies. Counterfactual scenarios undertaken for the United States suggest that

ECB Working Paper Series No 2248 / March 20198without the (cid:28)scal policy reaction, monetary policy would have needed to be signi(cid:28)cantly

more accommodative during the (cid:28)nancial crisis. Thereafter, however (cid:28)scal consolidation

has required monetary policy accommodation for longer.

Indeed, without the (cid:28)scal

consolidation undertaken after 2011, interest rates could have risen above the zero lower

bound already in 2013. This also implies that without monetary policy support the

strong (cid:28)scal consolidation would not have been possible without causing a signi(cid:28)cant

slowdown in US growth. .

The paper is organized as follows. Section 2 introduces the methodology and the data.

Section 3 reports the results. Additional robustness checks are conducted in section 4

while section 5 concludes.

2 Methodology and data

This section introduces the empirical model, the data and the identi(cid:28)cation strategy.

2.1 Empirical model and the data

We estimated the following reduced form VAR model for country c:

Ytci =

l
(cid:88)

p=1

Yt−p,ciβp

ci + ZtΦt + utci

utci ∼ N (0, Ωci)

(1)

(2)

where i denotes the group of countries (advanced vs. emerging economies), c= 1,2,...M

is the number of countries in each group and t=1,2,...T is the sample size. For the esti-

mation purpose we rely on a hierarchical panel VAR framework in the spirit of Jarocinski

(2010); a separate model is estimated for each group using Bayesian Methods.

Yc is a T × 5 matrix of endogenous variables for country c and includes a proxy for

government spending, GDP, in(cid:29)ation, tax and a monetary policy instrument. Govern-

ment spending measures real government consumption and investment (cid:21) i.e it excludes

ECB Working Paper Series No 2248 / March 20199the elements such as transfers (e.g. unemployment bene(cid:28)ts) which would depend on the

business cycle; we also exclude interest payments.1 Government revenues are measured

as taxes less transfers and (where possible) interest payments. The monetary policy

instrument is the policy interest rate.2 For the advanced economies we address the addi-

tional restrictions caused by the zero lower bound (ZLB) using a shadow interest rate for

the period from the end of 2008 (Wu and Xia, 2014 and Lemke et al. 2017). One concern

that might arise is that the ZLB regime could imply a change in the model parameters.

However, several studies that analyze the e(cid:27)ects of unconventional monetary policy seem

to suggest that model parameters have remained broadly stable despite the introduction

of unconventional measures (Gambacorta et al. 2014; Wu and Xia, 2016; Hachula et al.

2016).

To account for the world developments, we add Zt, a T × 5 matrix of exogenous vari-

ables common to all countries containing the VIX index of equity volatility, world GDP,

non-oil commodity prices, oil prices and a constant. It is worth noting that the exoge-

nous variables in Z enter the model at time t while all the other regressors represented

by a T × 5 × l matrix are lagged values of the endogenous variables Yc. All variables

are transformed in year on year growth rates, with the exception of the monetary policy

instrument. The data we use is at quarterly frequency and runs from 1998 to 2016 for

AEs (US, euro area, Japan and UK) and from 2000 to 2016 for EMEs (China, Brazil,

India, Russia). The lag length l is set to 5 for AEs and 6 for EMEs 3. We relegate to the

Appendix a detailed description of the data.

A key feature of this model is that it allows the β coe(cid:30)cients to vary across individual

countries as opposed to the standard pooled estimator which ignores cross section hetero-

1For EMEs group, due to the lack of data on government investment we rely on government con-

sumption to proxy the government spending.

2For the EMEs group, where the conduct of monetary policy has changed over time we add also the
monetary aggregate M2. This helps alleviate the price puzzle. A sensitivity analysis shows that this
adjustment has a rather limited impact on GDP response to policy shocks.

3Both the marginal Likelihood and Deviance Information Criteria (see Table S3) estimated separately
for each country prefer models with a number of lags greater than 4. We adopted 6 lags for EMEs since
it alleviates the price puzzle. For AEs the model with both 5 and 6 lags provides IRFs with correct sign
and similar magnitude but in the model with 6 lags GDP response to monetary policy shock display
some persistence which might have undesired e(cid:27)ects on the counterfactual scenario. As such for AEs we
prefer a model with 5 lags. The sensitivity section addresses the robustness of results to the lag length.

ECB Working Paper Series No 2248 / March 201910geneity. The unit speci(cid:28)c coe(cid:30)cients are obtained by imposing a hierarchical structure to

the model. Speci(cid:28)cally, it is assumed that the prior distribution for the VAR coe(cid:30)cients

βc is de(cid:28)ned as follows:

p(βci | ¯βi, λ) ∼ N ( ¯βi, λΩi)

(3)

where ¯βi are cross sectional average coe(cid:30)cients updated during the sampling pro-

cedure. Ωi is a Minnesota type variance which re(cid:29)ects the scale of the variables and

adjusts for the relative size of coe(cid:30)cient4. The crucial parameter in this setting is λ

which controls the degree of heterogeneity in the model. As λ → ∞ the coe(cid:30)cients

collapse to the country speci(cid:28)c VAR values while for λ = 0 the model is equivalent to

the pooled estimator. Ideally, λ should re(cid:29)ect a good balance between individual and
pooled estimates. In a standard Bayesian framework ¯βi and λ are calibrated parameters

while in the current context they are treated as random variables and have their own

distribution.

In brief, equation (3) reveals the prior knowledge that country coe(cid:30)cients are assumed

to be drawn from a common distribution centered around the cross sectional mean but

are allowed to deviate from this mean at a higher or lower degree dictated by the value of

the endogenously determined parameter λ which is common across units. Therefore, the

posterior of βc is a weighted average of the country OLS estimates and the prior mean

de(cid:28)ned in (3).

The hierarchical structure of the model o(cid:27)ers two key advantages that are relevant to

our study. First the group speci(cid:28)c average impulse response function can be computed
using the mean model coe(cid:30)cients ¯βi to obtain the estimates. This allows the comparison

of the GDP response to policy shocks in advanced versus emerging economies as a group.
Moreover, ¯βi contains information from the whole panel (and not only one country time-

series) and is updated during the sampling procedure; these features are likely to improve

the estimation precision. In addition, the hierarchical prior tends to shrink the country

4As per Litterman 1986, what matters for the size of a coe(cid:30)cient is the relative size of unexpected

change in the variable.

ECB Working Paper Series No 2248 / March 201911speci(cid:28)c coe(cid:30)cients towards the common mean leading to a more e(cid:30)cient use of the data

and more precise estimates of the unit speci(cid:28)c coe(cid:30)cients.

2.2 Priors

Following the approach suggested in Dieppe et al. (2017), standard di(cid:27)use priors are
assumed for ¯βi and Σci while Ωci is designed in a Minnesota type fashion. Regarding

λ, a traditional choice for the prior distribution is an inverse Gamma distribution with

shape parameter s0/2 and scale v0/2. Jarocinski (2010) and Gelman (2006) show that

results can be sensitive to the choice of the values for s0 and v0. As such, they suggest

the use of a uniform prior with s0 = -1 and v0 = 0 for models where the number of units

is greater than 5; or to make the prior weakly informative by using low values for s0 and

v0 (less than 0.001) which is the strategy adopted in this paper. A sensitivity analysis

shows that results are little a(cid:27)ected by the use of a uniform prior for λ instead.

2.3 Gibbs sampler

We rely on a Gibbs sampler to draw from the marginal posterior of the parameters. The

algorithm is based on Jarocinski (2010) and Dieppe et al. (2017) and it draws from the

following conditional posterior distributions:

• At iteration i draw ¯βi from a multivariate normal distribution:

N (βi−1

m , N−1Σi

b) with:

m=N−1(cid:80)N
βi
b=(cid:0)λi ⊗ Iq
Σi

c=1 βi−1
c
(cid:1) Ω where q is the number of coe(cid:30)cients to be estimated for each unit.

• At iteration i, draw λi from an inverse Gamma distribution :

λi∼IG( ¯s

2 , ¯v

2 ) with:

¯s=h +s0 where h is the number of coe(cid:30)cients to be estimated for all units.

¯v = v0 +(cid:80)N
c=1

(cid:110)(cid:0)βi−1

c − ¯βi(cid:1)(cid:48) (cid:0)Σ−1

c

(cid:1) (cid:0)βi−1

c − ¯βi(cid:1)(cid:111)

ECB Working Paper Series No 2248 / March 201912Draw Σi

b=(cid:0)λi ⊗ Iq

(cid:1) Ω

• At iteration i, draw βi

c for each country c from a multivariate normal distribution:

βi
c

∼ N (M, V ) with:
(cid:1)−1(cid:17)
(cid:104)(cid:16)(cid:0)Σi−1

c

M =V

yi + (cid:0)Σi

b

(cid:1)−1 ¯βi(cid:105)

V =

(cid:104)(cid:0)Σi−1

c

(cid:1)−1 ⊗ X (cid:48)

i Xi + (cid:0)Σi

b

(cid:1)−1(cid:105)−1

• At iteration i, draw Σi

c for each country c from the inverse Wishart distribution:

Σi
c

∼ IW (Sc, T ) with:

Sc= (cid:0)Yc − X cβi

c

(cid:1)(cid:48) (cid:0)Yc − X cβi

c

(cid:1)

We use 15000 replications as burn in sample and we save 10000 draws for inference,

discarding 99 draws for each one saved draw. 5

2.4

Identi(cid:28)cation strategy

We base our empirical results on the identi(cid:28)cation of two structural shocks, namely a

government spending shock and a monetary policy shock. The identi(cid:28)cation strategy

follows Blanchard and Perotti (2002) and Caldara and Kamps (2008) and it relies on the

recursive identi(cid:28)cation approach which implies timing restrictions on the contemporane-

ous impact across variables. The simplicity of this approach is particularly attractive for

our analysis since it is easily applicable to both advanced and emerging countries.

The variables enter the model in the following order: government spending, GDP,

in(cid:29)ation, tax and the monetary policy instrument. As such, in line with Blanchard

and Perotti (2002) it is assumed that government spending decisions are not a(cid:27)ected

5The estimation is conducted using a modi(cid:28)ed version of the BEAR toolbox of Dieppe et al. (2017)
which accomodates for mean model results estimation and convergence diagnostic test using ine(cid:30)ciency
factors.

ECB Working Paper Series No 2248 / March 201913contemporaneously by domestic business cycle developments6; therefore reduced form

innovations to the (cid:28)rst equation coincide with our identi(cid:28)ed government spending shock.

Turning to the monetary policy shocks, to achieve identi(cid:28)cation we order the monetary

policy instrument last and we allow for a contemporaneous reaction of the central bank to

(cid:29)uctuations in the other variables; this choice can be justi(cid:28)ed on the grounds of a central

bank following a Taylor rule in determining the interest rate. Although we control for

developments in government revenues, we do not aim at identifying a tax shock.

If

scholars seem to agree on the exogeneity of the government spending decisions, there is

less consensus on whether the tax variable should be ordered before or after the GDP,

making the identi(cid:28)cation of such shock problematic in a recursive framework.

3 Results

3.1 Model evaluation

To assess the reliability of the estimated panel model, we (cid:28)rst consider the model prop-

erties. The focus is on the response of GDP growth to innovations in both (cid:28)scal and

monetary policy variables. We report the (cid:28)scal multipliers from both the mean model

and the individual country estimates while for the monetary policy we show the mean

model IRFs in the main text and the country results in the Appendix (Figure S11). The

mean model results allow for the comparison of the IRFs in the two groups of countries,

while the single unit estimates tell us something more on the heterogeneity within group

of GDP response to policy shocks.

For government spending we convert the impulse response functions into (cid:28)scal mul-

tipliers to be able to compare them to the literature. Fiscal multipliers measure the

average change in real GDP from one unit (measured in national currency) increase in

government spending. Speci(cid:28)cally, we follow Blanchard and Perotti (2002) and we de-

(cid:28)ne multipliers as the ratio of the output response at a particular horizon to the impact

6We do, however, control for the contemporaneous developments at world level through the exogenous

variables.

ECB Working Paper Series No 2248 / March 201914e(cid:27)ect of the shock on the government spending. However, di(cid:27)erent countries reach the

peak response at di(cid:27)erent horizons, therefore in order to obtain comparable results across

countries we consider the average GDP response over the (cid:28)rst three years. Since data is

in growth rates, we (cid:28)rst convert the growth rates impulse responses to log-levels IRFs; we

then calculate multipliers by multiplying the log-level IRFs by the average ratio of gov-

ernment spending over the sample period. Table 1 reports the (cid:28)scal multipliers, averaged

over the (cid:28)rst three years, derived from the IRFs.

The mean model results suggest that (cid:28)scal multipliers are higher in AEs compared

to EMEs. This (cid:28)nding is consistent with previous studies such as Ilzetzki et al. 2013

and Kraay, 2012 who suggest that the degree of development is a critical determinant

of the size of the (cid:28)scal multiplier. They show that in developing countries, the response

of output to government consumption is often negative on impact and not statistically

di(cid:27)erent from zero.

Regarding the country speci(cid:28)c estimates, the government spending multipliers have

the expected sign, though di(cid:27)ering across countries. In the United States, the spending

multiplier is 1.3, in line with (cid:28)ndings from the literature. For example, Blanchard and

Perotti (2002) report a US spending multiplier in the range 0.9 and 1.3, while Ramey

(2011a and 2011b) points to a value between 0.6 and 1.2.

In Japan, the government

spending multiplier is within the ranges reported by Auerbach and Gorodnichenko (2014).

The government spending multiplier for the euro area is found to be quite small7. In

comparison, estimates of (cid:28)scal multipliers provided by Kilponen et al (2015) derived from

a large number of simulated structural models suggest a spending multiplier in the euro

area close to, but below 1. Turning to the United Kingdom, our spending multiplier

is higher that the (cid:28)ndings of Glocker et al (2017) who report an average (two-year

cumulative) government spending multiplier of 0.4, with, however, a signi(cid:28)cant variation

over time.

The literature provides fewer insights on (cid:28)scal multipliers in EMEs. The limited em-

7The low multiplier for euro area is driven by the initial (counterintuitive) negative response of GDP

to a spending shock.

ECB Working Paper Series No 2248 / March 201915Table 1: Government spending multipliers in the (cid:28)rst three years (average)

US Japan UK EA AEs China Brasil

India Russia EMEs

1.3

1.1

0.8

0.4

0.8

0.4

1.2

0.1

0.2

0.2

pirical literature suggests that (cid:28)scal multipliers in EMEs are smaller than in advanced

economies and often are not signi(cid:28)cant or even negative on impact (Ilzetzki et al. 2013,

Kraay, 2012). This (cid:28)nding is con(cid:28)rmed also by our results with the exception of Brazil

where the spending multiplier is 1.2. One potential explanation for this (cid:28)nding is that

Brazil’s economy is relatively closed, which tends to magnify the e(cid:27)ectiveness of its (cid:28)scal

policy. Finally, for China, it is worth noting that our measures may miss important

aspects of China’s (cid:28)scal policy. In particular, data do not allow us to capture o(cid:27)-balance

sheet spending by local governments which was a very important component of govern-

ment spending after the global (cid:28)nancial crisis.

Turning to the response of GDP growth to monetary policy shocks, the impulse

responses for monetary policy are also broadly in line with the literature. Figure 2 shows

the mean model IRFs for advanced and emerging countries to a 100 basis points increase

in the monetary policy interest rate in each country8. The contractionary measure has

the expected negative e(cid:27)ect on GDP growth for all countries. In advanced economies, the

peak impact is reached after around 4 quarters, but the e(cid:27)ect exhibits some persistence

in the (cid:28)rst three years. In emerging economies, the peak impact is slightly larger than for

advanced economies, but the response is less persistent. Note, however, that our model

may not fully capture monetary policies in all EMEs. For example, during the sample

period China used a combination of quantity and price tools to enact monetary policy.

In particular, the use of window guidance to bolster credit growth following the global

(cid:28)nancial crisis, would not be captured.

Contrary to some of the literature for small-scale VARs (see Ramey, 2016), we do

not (cid:28)nd evidence of a ‘price puzzle’ for advanced economies, as the response of in(cid:29)ation

to a monetary policy shock is negative (see Figure S9 in the Appendix). Overall, this

8The country speci(cid:28)c IRFs for monetary policy are similar to the mean model estimates and are not

reported in the main text for ease of exposition. They are available in the Technical Appendix.

ECB Working Paper Series No 2248 / March 201916provides some comfort that our monetary policy shocks are correctly identi(cid:28)ed in our

VAR. By contrast, for EMEs, there is evidence of a mild, short-lived price puzzle which

sees the in(cid:29)ation rise temporarily after a contractionary monetary policy shock.

It is worth noting that GDP response to (cid:28)scal shocks displays more heterogeneity

(between and within group) compared to the GDP response to monetary policy shocks9.

Regarding the monetary policy (cid:28)nding, our results are in line with Jarocinski (2010) and

Mojon and Peersman (2001) who show that the e(cid:27)ects of monetary policy tend to be

even across groups with substantial structural di(cid:27)erences. In contrast, other studies such

as Cecchetti (1999) and Mihov (2001) (cid:28)nd asymmetries in monetary transmission among

countries.

On the other side, the variability of (cid:28)scal multipliers is not new in the literature.

Several studies suggest that (cid:28)scal multipliers depend on the economic conditions or on

the speci(cid:28)c sample analyzed. In particular, Ilzetzki et al. (2013) claim that (cid:28)scal multi-

pliers depend on the degree of openness of a country, on the level of debt, the exchange

rate regime and on the level of development. Corsetti et al. (2012) highlight important

di(cid:27)erences in the transmission of spending shocks across countries conditional on the

exchange rate regime, the health of public (cid:28)nances and the occurrence of (cid:28)nancial crisis.

They (cid:28)nd higher multipliers during (cid:28)nancial crises and in countries with (cid:28)xed exchange

rate regimes, while the weakness of public (cid:28)nances is shown to have a negative impact

on spending multipliers. Nickel and Tudyka (2014) focusing on a sample of 17 Euro-

pean countries show that spending multipliers vary considerably with the debt-to-GDP

ratios, and can even turn negative at higher levels of debt. Gechert and Rannenberg

(2014) reveal increasingly smaller e(cid:27)ects of (cid:28)scal shocks as the economy is further above

its potential (as (cid:28)scal measures tend, in these circumstances, to crowd-out rather than

crowd-in the private sector). Whalen and Reichling (2015) distinguish speci(cid:28)c multiplier

ranges for when the economy has an active monetary policy. They point out that: (i)

multiplier values are lower under more active monetary policy, which o(cid:27)sets the e(cid:27)ects

9We refer to heterogeneity of one policy relative to the other since the parameter λ governing the

model heterogeneity is common across units of the same group

ECB Working Paper Series No 2248 / March 201917Figure 2:
year-on-year GDP growth to a 1pp increase in the monetary policy interest rate

Impulse responses to monetary policy shocks. Mean model results. Response of

(a) Advanced economies

(b) Emerging economies

of the (cid:28)scal policy measures, stabilising the economy; and that (ii) more credible and/or

longer lasting measures usually imply greater e(cid:27)ect on output. Coenen et al. (2012) cor-

roborate some of these (cid:28)ndings in a structural model with an accommodative monetary

policy. Finally, studies focusing on non-linearities such as Auerbach and Gorodnichenko

(2012a) and Mumtaz and Sunder-Plassmann (2017) report higher multipliers in recession

compared to boom.

3.2 Conditional forecasts to evaluate the role of policy support

To discern the e(cid:27)ect of (cid:28)scal and monetary policy on GDP growth, we compare model

forecasts conditioned on actual policy developments with forecasts conditioned on a coun-

terfactual policy path. The exercise consists of construction of conditional forecast for

GDP growth under two counterfactual scenarios: an actual policy scenario and a coun-

terfactual policy scenario:

1. Under the actual policy scenario we produce a path for GDP conditional on the

actual realizations of policy variables.

2. In the counterfactual policy scenario, the actual values of the policy variables are

replaced by their sample averages over the estimation period.

ECB Working Paper Series No 2248 / March 201918To assess the contributions of policy to economic developments, we then compare the

median outcomes of the two scenarios (cid:21) i.e. we subtract the conditional path for GDP

growth in the actual policy scenario from the conditional path in the counterfactual policy

scenario.

We (cid:28)rst compare the combined impact of (cid:28)scal and monetary policies - i.e. we con-

duct a counterfactual policy scenario in both the (shadow) interest rate and government

spending variables are constrained to their sample averages over the estimation period.

We then look at the contributions of (cid:28)scal and monetary policies separately. To examine

the contributions of monetary policy, we restrict only the path of the interest rate in the

counterfactual policy scenario. To examine the contribution of (cid:28)scal policy, we restrict

only the path of government spending.

The approach is illustrated for the United States in Figure 3. The left-hand side

chart shows the actual path of the (shadow) interest rate and the counterfactual policy

path (set to the sample average for the interest rate in the United States). The right-

hand side chart then shows the GDP conditional forecast based of the actual policy rate

(blue line) and the GDP path conditional on counterfactual policy (green line). Both

scenarios imply a deep decline in 2008 and 2009 which re(cid:29)ects the e(cid:27)ects of the global

factors captured by the exogenous variables in the VAR. However, gradually di(cid:27)erences

in the path of (year-on-year) GDP growth emerge. Those di(cid:27)erences (measured in the

percentage point di(cid:27)erences of GDP growth) are shown by the gray bars and aim to

capture the policy impact of monetary policy in that period.

Note that we deliberately compare two conditional forecast scenarios (cid:21) i.e. we com-

pare the counterfactual policy scenario with another conditional forecast for GDP based

on the actual realisations of policy variables. Another option would have been to com-

pare the counterfactual policy scenario with the actual realizations of GDP (i.e.

the

green line with the red line in Figure 3). But this strategy would have mixed the e(cid:27)ects

of policy with other factors that generated (cid:29)uctuations in real activity over this period.

Our method aims to isolate the policy contributions.

Note also that we employ a structural approach to understanding the contributions of

ECB Working Paper Series No 2248 / March 201919Figure 3: United States: Counterfactual scenario for monetary policy

(a) United States: Actual and counterfactual policy
realisation for monetary policy (lines (cid:21) percent; bars (cid:21)
percentage point di(cid:27)erences)

(b) United States: Actual and counterfactual policy
realisation for GDP growth (year-on-year percentage
changes, lhs; percentage point di(cid:27)erences, rhs)

policy. Lenza et al. (2010) and Kapetanios et al. (2012) use a similar approach to analyze

the e(cid:27)ects of quantitative easing in the euro area and UK respectively. However, they use

a reduced form approach in which the path of the restricted variable is obtained through

the contribution of all shocks. By contrast, we employ a strategy in which restrictions

on speci(cid:28)c structural shocks generate the (cid:28)xed path of the conditioned variable. For

example, in order to obtain the GDP forecast with the federal fund rate (cid:28)xed at a

predetermined value, we restrict (only) the monetary policy structural shock in such a

way that it generates the desired (cid:28)xed path for the monetary instrument (see Doan et

al. 1983 and Waggoner and Zha 1999, Dieppe et al. 2017); no restrictions are placed on

the other shocks which are drawn from their own distribution.10 The main advantage of

the structural approach in conducting the counterfactual analysis is that it captures the

heterogeneity (across countries) of the policy contribution on GDP by taking into account

the variability in the response of GDP to policy shocks as well as in the design of the

speci(cid:28)c policy measure. A detailed example of the counterfactual scenario is presented

in the appendix.

The estimated overall support from government spending and monetary policy is

10For example, in case of a recursive identi(cid:28)cation, an unrestricted shock is drawn from a N(0,1)

distribution. See Dieppe et al. 2017 for details.

ECB Working Paper Series No 2248 / March 201920Figure 4: Policy contributions to aggregate GDP for eight countries (Percentage point di(cid:27)erence
in year-on-year GDP growth between actual policy and counterfactual policy scenarios)

Notes: the lines show the di(cid:27)erences year-on-year GDP growth between actual policy and counterfactual policy

scenarios (see section 3.2 for explanation). Green line shows the impact of (cid:28)scal and monetary policies combined;

red dotted line shows impact of (cid:28)scal policy only; blue shows the impact of monetary policy. GDP growth is a

PPP-weighted average of the 8 countries in the sample

shown in Figure 4 for the aggregate GDP of the countries in the sample, and at country

level in Figure 5.11 The results suggest that global activity bene(cid:28)ted from substantial pol-

icy support in the aftermath of the global (cid:28)nancial crisis, as policymakers loosened both

(cid:28)scal and monetary policy to combat the sharp downturn in economic activity. Moreover,

the policy support faded quickly, and by 2011, policy acted as a drag on global activity.

The shift was mostly driven by (cid:28)scal policies: while monetary policy remained accom-

modative, particularly in advanced economies, e(cid:27)orts towards consolidation provided a

signi(cid:28)cant headwind to the global expansion. More recently, macroeconomic policies

have become more supportive for global activity. The drag from (cid:28)scal policies consolida-

tion has gradually lessened, particularly in advanced economies. With monetary policies

remaining highly accommodative in advanced economies and some monetary easing in

large EMEs, the overall contribution of policy to growth has shifted and become less

11For ease of exposition we limit our attention to the di(cid:27)erences in the medians across the two scenarios.
Figures S17 and S18 in the appendix report the full posteriors for both scenarios for the case of overall
policy contribution.

ECB Working Paper Series No 2248 / March 201921Figure 5: Policy contributions to GDP growth for advanced and emerging economies
(Percentage point di(cid:27)erence in year-on-year GDP growth between actual policy and counter-
factual policy scenarios)

(a) Advanced economies

(b) Emerging economies

Notes: the lines show the di(cid:27)erences year-on-year GDP growth between actual policy and counterfactual policy

scenarios (see section 3.2 for explanation).

negative.

The extent of policy support has varied strongly across countries and instruments, in

particular after the initial support to the global (cid:28)nancial crisis.

Speci(cid:28)cally, in advanced economies (Figure 6), highly accommodative monetary pol-

icy has been counteracted by strong (cid:28)scal consolidation. After the initial support pro-

vided following the Great Recession, the support from (cid:28)scal policy in advanced economies

faded quite rapidly, acting as a signi(cid:28)cant drag on economic activity.

In the United States, federal spending as part of the American Recovery and In-

vestment Act started to wane, while state and local government spending continued

to diminish from 2011 onwards, re(cid:29)ecting the states’ balanced budget rules. In 2012-

13 some (cid:28)scal measures expired (including the Bush income tax cuts for high-income

households, the payroll tax reduction for middle-income households; and the extended

unemployment bene(cid:28)ts). More recently, however, policy has provided a more supportive

backdrop: the drag from (cid:28)scal consolidation has eased, while monetary policy remained

accommodative. In Japan, (cid:28)scal consolidation was delayed by the earthquake in 2011

ECB Working Paper Series No 2248 / March 201922Figure 6: Policy contributions to GDP growth for advanced economies from monetary and
(cid:28)scal policies (Percentage point di(cid:27)erence in year-on-year GDP growth between actual policy
and counterfactual policy scenarios)

(a) Monetary policy

(b) Fiscal policy

Notes: the left-hand side chart shows the di(cid:27)erences in year-on-year GDP growth between actual policy and

counterfactual policy scenarios in which only the interest rate is restricted in the counterfactual policy scenario. the

right-hand side chart shows the di(cid:27)erences in year-on-year GDP growth between actual policy and counterfactual

policy scenarios in which only the path of government spending is restricted in the counterfactual policy scenario.

See section 3.2 for explanation.

ECB Working Paper Series No 2248 / March 201923Figure 7: Policy contributions to GDP growth for emerging market economies from monetary
and (cid:28)scal policies (Percentage point di(cid:27)erence in year-on-year GDP growth between actual policy
and counterfactual policy scenarios)

(a) Monetary policy

(b) Fiscal policy

Notes: the left-hand side chart shows the di(cid:27)erences in year-on-year GDP growth between actual policy and coun-

terfactual policy scenarios in which only the interest rate is restricted in the counterfactual policy scenario. The

right-hand side chart shows the di(cid:27)erences in year-on-year GDP growth between actual policy and counterfactual

policy scenarios in which only the path of government spending is restricted in the counterfactual policy scenario.

See section 3.2 for explanation.

which necessitated emergency spending. Monetary policy by the Bank of Japan through

its quantitative easing program has increasingly supported GDP growth over the sample

period. In the United Kingdom, the contribution from monetary policy has been a pillar

of growth, but has become more neutral recently as the Bank of England has started to

gradually remove its policy accommodation. Meanwhile (cid:28)scal policy has also become less

of a drag over time after a long period of austerity. Finally, in the euro area (cid:28)scal policy

was a major drag on growth. This can be explained by consolidation needs that arose

due to the euro area sovereign debt crisis. However, the drag from (cid:28)scal consolidation

has also diminished here. By contrast, monetary policy in the euro area has supported

growth.

On the other side, in EMEs (Figure 7), monetary policy has been less accommodative

since the global recession. In China, after the initial policy support during the global re-

cession monetary policy tightened, with interest rates and reserve requirements remaining

ECB Working Paper Series No 2248 / March 201924relatively high despite low in(cid:29)ation. Subsequently, as GDP growth slowed during 2014,

lower interest rates and some (cid:28)scal support have provided for more supportive policy.

However, it is likely that our model does not fully capture the role of (cid:28)scal policy in

China. IMF (2017c) estimates a signi(cid:28)cantly larger (cid:28)scal de(cid:28)cit than reported by o(cid:30)cial

(cid:28)gures, suggesting substantially bigger (cid:28)scal support. Amongst other EMEs, the expe-

rience of commodity exporting EMEs (Brazil and Russia) has played an important role

in shaping developments. Policy in these countries was broadly supportive for activity

until 2014 when sharp terms of trade shocks forced a recalibration of policies. Monetary

policy tightened in both countries to combat currency depreciation and high in(cid:29)ation

and in(cid:29)ation expectations. Fiscal policies were also restrained (cid:21) by high debt and weak

credibility in the case of Brazil; and by the need to adjust to lower oil revenues in the

case of Russia. With (cid:28)scal consolidation remaining a necessity in both countries, poli-

cies continue to act as a headwind to GDP growth, although some monetary easing - as

currencies have stabilised and in(cid:29)ation has fallen - has provided some help.

3.3 The interaction of monetary and (cid:28)scal policies

The empirical setting used in our analysis is well suited to analyzing the interaction and

interdependencies of monetary and (cid:28)scal policies. In this section, we ask two questions:

(i) how might monetary policy (MP) have behaved if (cid:28)scal policy (FP) had been con-

ducted di(cid:27)erently? and (ii) how strong would (cid:28)scal support have needed to be, had

monetary policy been less accommodative? We illustrate the interactions using the US

economy as an example, being one of the largest economies in our sample. We (cid:28)rst assess

the role of (cid:28)scal policies in shaping monetary policy since the global (cid:28)nancial crisis. Fol-

lowing the methodology described in section 3.2 we conduct two conditional scenarios.

In the (cid:28)rst scenario we restrict shocks to government spending, tax, in(cid:29)ation and GDP

to produce a conditional forecast for interest rates conditioned on the observed paths of

GDP, in(cid:29)ation, tax and government spending. In the second scenario, we restrict the

same shocks to obtain a path for interest rates conditioned on the actual values of GDP,

in(cid:29)ation and tax but keeping government spending at its sample average (Figure 8 a). To

ECB Working Paper Series No 2248 / March 201925compensate for the lack of the (cid:28)scal stimulus, the counterfactual paths for policy suggest

a sharper reduction in (shadow) interest rates in the United States. Thereafter, however,

(cid:28)scal consolidation has forced continued accommodation from monetary policy. Without

the (cid:28)scal consolidation that occurred from 2011 onward, monetary policy would have

begun to tighten already in the United States (cid:21) indeed, our model suggests that interest

rates would have been above the zero lower bound already in 2013. In other words, the

results show how much of the monetary policy reaction was triggered by the additional

e(cid:27)ects on output and in(cid:29)ation generated by the government spending policy.

We next consider the reverse question and ask how (cid:28)scal policy might have behaved

had monetary policy been di(cid:27)erent. As before, we conduct two scenarios. In the (cid:28)rst sce-

nario we restrict shocks to GDP, in(cid:29)ation, tax and interest rates to produce a conditional

forecast for government spending conditioned on the observed paths of GDP, in(cid:29)ation,

tax and interest rates (blue lines in Figure 8 b). In the second scenario, we restrict the

same shocks to obtain a path for government spending conditioned on the actual values

of GDP, tax and in(cid:29)ation but keeping interest rates at the sample averages (red lines

in Figure 8 b). The counterfactual scenarios highlight the role of accommodative mone-

tary policies in allowing (cid:28)scal consolidation in the US after the global recession. In the

scenarios without considerable monetary accommodation (i.e. the red lines), the model

suggests that government spending would have needed to be stronger to support activity.

4 Robustness analysis

We perform additional robustness checks aiming to address some of the concerns raised

by our analysis. More details on the sensitivity tests discussed in this section can be

found in the appendix to the paper (Figures S12 - S16). Jarocinski (2010) and Gelman

(2006) show that weekly informative prior for the parameter λ governing the model

heterogeneity can have undesired e(cid:27)ects on results, especially for panel with more than

5 units. In order to reinforce our results, we re-estimate the model using the uniform

prior (with s= -1 and v=0) instead. Impulse response functions reported in Figure S12

ECB Working Paper Series No 2248 / March 201926Figure 8: Policy interaction scenarios for the United States

(a) Monetary policy (percent)

(b) Fiscal policy (year-on-year percentage changes)

Notes: the left-hand side chart shows the actual and counterfactual paths for monetary policy from 2007. The

red line shows the actual (shadow) interest rate path. The blue line shows the conditional forecast for the interest

rate, conditioned on the observed pro(cid:28)les for GDP, in(cid:29)ation and government spending (from 2007 onwards).

The green line shows the conditional forecast for the interest rate, conditioned on the observed pro(cid:28)les for GDP,

in(cid:29)ation (from 2007) and with government spending (cid:28)xed at the sample average. The right-hand side chart shows

the actual and counterfactual paths for government spending from 2007. The red line shows the actual path of

government spending (in year-on-year percentage changes). The blue line shows the conditional forecast for the

government spending, conditioned on the observed pro(cid:28)les for GDP, in(cid:29)ation and the interest rate (from 2007

onwards). The green line shows the conditional forecast for government spending, conditioned on the observed

pro(cid:28)les for GDP, in(cid:29)ation (from 2007) and with the interest rate (cid:28)xed at the sample average.

ECB Working Paper Series No 2248 / March 201927Appendix are almost una(cid:27)ected by this change.12

In choosing the lag structure for EMEs, we prefer a model with 6 lags. In addition

we control for the monetary aggregate including M2 before monetary policy instrument.

This model performs better in alleviating the price puzzle. However, for the AEs we

adopt a 5 lag structure without M2 and there might be concerns on the validity of the

statements regarding the comparison of policy e(cid:27)ects across groups. Moreover, there

is not a clear agreement in the literature on whether M2 should be placed before or

after the policy rate. As such we check the e(cid:27)ect of policy shocks on GDP in EMEs

in two additional scenarios, speci(cid:28)cally with 5 lags instead of 6 and with M2 ordered

last. IRFs of GDP are mildly a(cid:27)ected while price puzzle is a bit more pronounced in

both cases. Additionally, since EMEs have to deal also with the excessive money growth

rooted in the government‘s need to (cid:28)nance itself by seignorage (see Frankel, 2010), we

perform a counterfactual check for EMEs in which monetary policy targets both the

interest rate and the monetary aggregate. Results (see Figure S14) show an increase in

the magnitude of the e(cid:27)ects of monetary policy compared to the scenario of only interest

rate targeting. We also test the sensitivity of our results to employing a di(cid:27)erent shadow

rate for AEs in order to account for the ZLB. Figure S16 shows impulse responses of

GDP for advanced economies to a monetary policy shock using the shadow rate measure

proposed by Krippner. Finally, we check the convergence of the Gibbs sampler reporting

the ine(cid:30)ciency factors for the posterior estimates of the parameters. The convergence

diagnostics (Figure S19) are satisfactory with ine(cid:30)ciency factors values below 5 for all

parameters.

5 Conclusion

We used Panel VARs with hierarchical structure to asses and compare the e(cid:27)ects of (cid:28)scal

and monetary policy to GDP growth in advanced and emerging economies. Our results

suggest that the e(cid:27)ects of monetary policy on GDP are similar across the two groups

12Since the counterfactual scenario is constructed from pieces of IRFs, we are comfortable to assume

that stable IRFs imply stable conditional forecasts.

ECB Working Paper Series No 2248 / March 201928while the (cid:28)scal multipliers are higher in AEs compared to EMEs. We also (cid:28)nd that (cid:28)scal

policy e(cid:27)ects display some within group variation. This e(cid:27)ect is not veri(cid:28)ed in the case

of monetary policy.

We then conducted a counterfactual analysis and we provided evidence that global

GDP growth bene(cid:28)ted from substantial policy support during the global (cid:28)nancial crisis

but policy tightening thereafter, particularly (cid:28)scal consolidation, acted as a signi(cid:28)cant

drag on the subsequent global recovery.

In addition we show that the role of policy

has di(cid:27)ered across countries. Speci(cid:28)cally, in advanced economies, highly accommodative

monetary policy has been counteracted by strong (cid:28)scal consolidation. By contrast, in

EMEs, monetary policy has been less accommodative since the global recession.

Finally, our counterfactual scenarios emphasize the important interdependence of

(cid:28)scal and monetary policies in shaping each other. The scenarios provide admittedly

stark contrasts but they underscore the interdependence of each branch of macroeconomic

policy. In particular, in United States, we (cid:28)nd that (cid:28)scal consolidation in the aftermath

of the (cid:28)nancial crisis has pushed continued accommodation from monetary policy.

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Conditional forecast example

Imagine the policymaker wants to answer the following question:

What is the forecast of GDP conditioned on knowing that Federal Fund rate in the next 2

periods is 1%?

Step1.

De(cid:28)ne a simple 2 variables VAR as in (4) formed by GDP (Y) and Interest

rate (X).

ECB Working Paper Series No 2248 / March 201934



Yt

Xt





 =



c1

c2





 +



B1 B2

B3 B4









Yt−1

Xt−1





 +



A11

A21 A22













e1t

e2t

(4)

and de(cid:28)ne zij

k as the IRF of j to K shock at horizon i and variable X is constrained to be 1 in

the next two periods.

R =





z1
1,2
z1
2,2

2

z1,2

0

0

z2
2,2

z1
1,2

z2
1,2









r =

1 − ¯Xt+1
1 − ¯Xt+2





(5)

(6)

where ¯X denotes the unconditional forecast of X and r is the di(cid:27)erence between the desired

path of the Federal Fund rate and its unconditional forecast.

Step2.

Re-write the desired restriction in terms of structural shocks e and the matrix

of impulse responses R

Re = r

(7)

As per Waggoner and Zha (1999) draw the restricted shock e from a distribution:

e ∼ N (cid:0)R(cid:48)(RR(cid:48))−1r, I − R(cid:48)(RR(cid:48))−1R(cid:1)

(8)

If we restrict all structural shocks in (7) we get a reduced form solution. If instead we want

to attribute the desired path to a speci(cid:28)c shock, for example the Monetary policy shock, we draw

the restricted shock e2 from (8) while the remaining shock is drawn from its own distribution

which is a N(0,1) in a recursive scenario.

Data description

Data transformations:

• Government spending: Government consumption and government investment de-

(cid:29)ated by the GDP de(cid:29)ator - annual growth rates.

• Taxes: Current receipts minus transfers and interest payment de(cid:29)ated by the GDP

de(cid:29)ator (cid:21) annual growth rates.

ECB Working Paper Series No 2248 / March 201935Figure S9: In(cid:29)ation response to a 1% increase in interest rate. Mean model results

In(cid:29)ation AEs

In(cid:29)ation EMEs

• Real GDP (cid:21) annual growth rates.

• In(cid:29)ation (cid:21) annual growth in CPI

• VIX - CBOE Market Volatility Index (cid:21) levels

• Commodity Price Index: All Commodities (C001CXAP@IFS) (cid:21) annual growth

rates

• World: Energy Index (C001CXE@IFS) (cid:21) annual growth rates

• World GDP (GDP for world, weighted by PPP) minus country GDP - annual

growth rates

ECB Working Paper Series No 2248 / March 201936Table S2: Monetary and Fiscal Data description (with HAVER codes, where applicable)

Table S3: Marginal likelihood/ Deviance Information criteria for single country VAR. Models
with higher marginal likelihood and smaller Deviance Information Criteria are preferred.

Lags

US

Jap

UK

EA

China

Brasil

India

Russia

Test

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

Mg Lik/DIC

4

5

6

-145/1042

-223/926

-264/1129

-177/730

-299/1309

-316/1373

-376/1651

-405/1800

-239/1010

-219/907

-261/1113

-172/707

-294/1283

-311/1347

-370/1623

-398/1766

-235/992

-218.45/894

-257/1094

-170/699

-290/1265

-307/1325

-364/1598

-392/1738

ECB Working Paper Series No 2248 / March 201937Figure S10: Interest rate and in(cid:29)ation response to a 1% increase in government spending.
Mean model results

Interest rate AEs

Interest rate EMEs

In(cid:29)ation AEs

In(cid:29)ation EMEs

ECB Working Paper Series No 2248 / March 201938Figure S11: GDP response to (cid:28)scal and monetary shocks. Country results

Monetary policy AEs

Fiscal policy AEs

Monetary policy EMEs

Fiscal policy EMEs

Figure S12: Sensitivity analysis to the prior on λ.

ECB Working Paper Series No 2248 / March 201939Figure S13: Sensitivity analysis to the number of lags for EMEs. Country results with 5
lags.

Monetary policy

Fiscal policy

Figure S14: Monetary Policy Contribution in EMEs with M2 and R targeting

ECB Working Paper Series No 2248 / March 201940Figure S15: Sensitivity analysis to variables ordering for EMEs. Results with M2 ordered
last.

Monetary policy

Fiscal policy

Figure S16: Sensitivity analysis to using Krippner shadow rate

-0.4-0.3-0.2-0.10.00.10.20.30.40.50.616111621263136GDP response to MP shock-0.4-0.3-0.2-0.10.00.116111621263136Inflation response to MP shockECB Working Paper Series No 2248 / March 201941Figure S17: Conditional forecast AEs. Overall policy scenarios. Bands are 68 HPDI

Figure S18: Conditional forecast EMEs. Overall policy scenarios. Bands are 68 HPDI

ECB Working Paper Series No 2248 / March 201942Figure S19: Ine(cid:30)ciency factors

ECB Working Paper Series No 2248 / March 201943Acknowledgements 

We are grateful to Haroon Mumtaz for his valuable advice and extended comments. We also thank the authors of the BEAR Toolbox, 
Matteo Ciccarelli, Georgios Georgiadis, Marek Jarocinski, Giorgio Primiceri, the participants of Rimini Bayes conference and the 
DG-I /EXT Analytical meetings at the ECB and an anonymous referee for useful comments. 

Ursel Baumann 
European Central Bank, Frankfurt am Main, Germany; email: ursel.baumann@ecb.europa.eu 

David Lodge 
European Central Bank, Frankfurt am Main, Germany; email: david.lodge@ecb.europa.eu 

Mirela S. Miescu 
Queen Mary University of London, London, United Kingdom; email: m.s.miescu@qmul.ac.uk 

© European Central Bank, 2019 

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