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Economic Interferences
AE
Vol. 25 • No. 63 • May 2023 575
WHEN INFLATION AGAIN MATTERS: DO DOMESTIC AND GLOBAL
OUTPUT GAPS DETERMINE INFLATION IN THE EU?
Jana Budová1
*
, Veronika Šuliková2 and Marianna Siničáková3
1)2)3) Technical University of Košice, Košice, Slovakia
Please cite this article as:
Budová, J., Šuliková, V. and Siničáková, M., 2023. When
Inflation Again Matters: Do Domestic and Global Output
Gaps Determine Inflation in the EU?. Amfiteatru
Economic, 25(63), pp. 575-592.
DOI: 10.24818/EA/2023/63/575
Article History:
Received: 25 November 2022
Revised: 6 March 2023
Accepted: 3 April 2023
Abstract
In this paper, we examine whether domestic or global output gap affects inflation in three
panels: the European Union, the peripheral countries of the European Union, and the
Eurozone. We have also analysed the impact of these variables on inflation in individual
countries of the European Union. To find the determinants of inflation, we employ the
Granger causality test and panel regression. The first examined period is from 1Q 1997 to
3Q 2020. The period between 1999 and 2020 is divided into two shorter periods – the
precrisis (1999 – 2008) and postcrisis (2009-2020) period. The results of the study show that
after the crisis the global output gap predicts the evolution of inflation in the Eurozone panel.
On the other hand, the domestic output gap predicts inflation in the European Union. In the
precrisis period, the determinant of inflation is the domestic output gap, specifically in the
Eurozone panel. In the European Union panel and its peripheral economies, the global output
gap determines inflation. In Italy, Lithuania, Estonia, Finland, Latvia, and the Netherlands,
the domestic output gap determines inflation. The global output gap determines inflation in
the Netherlands, Slovenia, Estonia, and Latvia. We demonstrated that there are two ways
dependency among the variables.
Keywords: domestic output gap, global output gap, inflation, the Granger causality test,
panel data model.
JEL Classification: E31, E32, E58.
*
Corresponding author, Marianna Siničáková – e-mail: marianna.sinicakova@tuke.sk
This is an Open Access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited. © 2022 The Author(s).
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When Inflation Again Matters: Do Domestic
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576 Amfiteatru Economic
Introduction
After a decade of exceptionally low inflation in the Eurozone well below the ECB (European
central bank) target (according to Eurostat, the average inflation rate was 1.2% from 2012 to
2021), inflation issues reappeared at the end of 2021 and accelerated in 2022 (8.1% in May
2022). The situation is similar beyond the Eurozone. Numerous reports from other countries
warn against the negative effects of price jumps, e.g., the inflation rate in the US hit 8.6% in
May 2022, which is the highest rate among G7 countries (Sherman, 2022). Reasons for these
inflation pressures stem from slow central bank responses; too long-lasting practise of
quantitative easing; release of pandemic measures; lack of certain resources, raw materials,
and stocks; business cycles, the war in Ukraine; deglobalization changes in the world, etc. It
will be crucial to identify crucial inflation determinants to curb inflation pressures.
An essential indicator that helps policy makers to predict the behaviour of inflation is the
output gap. This indicator is not easily observable, it must be estimated (for example, by
means of a Hodrick-Prescott (HP) filter, a multivariate HP filter, a production function
approach, a DSGE model, etc.). The formation of the relationship between these two
variables dates to the middle of the 20th century; at present, the basis of this relationship is
the New Keynesian Phillips curve (NKPC). The global output gap (GOG) appears to be a
very important variable that can explain inflationary behaviour. The significant relationship
between these variables may be mainly due to the integration of world markets that has taken
place in recent years. This raises the question for many authors whether the determinant of
inflation is the global output gap, the domestic output gap (DOG), or both.
The impact of both output gaps on inflation was analysed in an article by Jašová et al. (2020).
The authors examined the impact of these two variables on inflation in two different groups
of countries. The first group consisted of advanced countries, while the second group
consisted of emerging economies. Several authors have dealt with the same issue, for
example Çiçek (2012), Bianchi and Civelli (2015), Łyziak (2019), Busetti et al. (2021) and
many others. The aim of the paper is to fill in the gap in existing research by estimating panel
models that also include the global output gap and to compare the results of different
methods. To our best knowledge, only five papers consider global output gaps in their models
(Çiçek, 2012; Bianchi & Civelli, 2015; Łyziak, 2019; Jašová et al., 2020; Busetti et al., 2021)
and other authors consider only domestic output gaps (Assenmacher-Wesche et al., 2008;
Kendera, 2015).
This paper includes three objectives. First, we want to determine whether, in the current
globalised world, inflation is influenced by the global rather than the domestic output gap.
Subsequently, we want to identify other determinants of inflation. The final objective is to
determine whether the impact of output gaps on inflation has changed in the post-crisis period
(i.e. after 2008).
In this paper, we analyse whether inflation is determined by output gaps not only in specific
countries of the European Union (EU), but also in selected panels. The paper analyses
whether there is not only one-way but also two-way dependence between the variables. As
inflation is currently in the centre of discussion, we are also trying to identify other
determinants. The paper deals with the identification of inflation determinants in selected
panels before the crisis in 2008 and in the post-crisis period.
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Vol. 25 • No. 63 • May 2023 577
1. Review of the scientific literature
There are currently many studies that examine the determinants of various macroeconomic
variables. The interest in research in the field of driving forces of inflation is growing over
time; the authors are trying to use various methods to determine its determinants. The subject
of the research of most authors is one specific country or Eurozone; a small part of the
contributions is focused on groups of countries including developed and emerging economies
or the Visegrad group of countries. In most studies, the global output gap, the domestic output
gap, the exchange rate, as well as interest rates appear to be important indicators of inflation.
The euro area countries were already researched by Gerlach & Svensson in 2003. The data
on the indicators date back to 1980 when the Eurozone was not yet established. In 2018,
Jarociński and Lenza (2018) dealt with inflation indicators in the Eurozone. The authors
Busetti et al. (2021) used various methods to conclude that the output gap is the driving force
behind inflation in the Eurozone. In addition to other variables that enter the econometric
model, the authors mentioned above used the output gap as another variable. To avoid biasing
the results, the econometric model must include control variables (Busetti et al., 2021).
The determinants of Chinese inflation, and specifically the impact of the output gap on this
inflation, have been addressed in studies by the following groups of authors: Gerlach and
Peng (2006), Zhang and Murasawa (2011), Zhang et al. (2017), Wang et al. (2022). Zhang et
al. (2017) choose the global output gap as a potential determinant of inflation in China. The
global output gap was measured by the weighted output gap of the 18 best Chinese trading
partners. The authors recommend that the Bank of China address the impact of this global
output gap because inflation would be easier to predict. The exchange rate was the indicator
used in addition to the global output gap and the M2 monetary aggregate by the authors.
Gerlach and Peng (2006) used this indicator only as an unobserved variable due to the
difficult measurement of the impact on inflation.
Valadkhani (2014) and Tiwari et al. (2014) focused on advanced economies such as France
and a group of countries: the United Kingdom, the US, and Canada. The period examined
was rather the same. The authors chose the output gap as a variable that could be a
determinant of inflation. Valadkhani (2014) also used the following indicators: wage rate, oil
prices, and the nominal effective exchange rate (NEER). The authors of both studies pointed
to the same result; the output gap determined inflation. Kendera (2015) focused on the
determinants of inflation in the Visegrad Four countries. Łyziak (2019) focused on one of
these countries - Poland. Kendera (2015) used a vector autoregressive model and the Granger
causality test. The aim of both authors was to find out whether the output gap indicates
inflation. Łyziak (2019) used both output gaps as determinants, i.e., the DOG and the GOG.
Macroeconomic variables such as the interest rate and the exchange rate were included in the
model by Kendera (2015), and in addition to these two variables, the author added the
domestic output gap to his analysis. The global output gap loses its relevance in a model in
which inflation is specified as core, i.e. it excludes food and energy (Łyziak, 2019).
Assenmacher-Wesche & Gerlach (2008), Bjørnland et al. (2008), and Çiçek (2012) dealt with
the impact of output gaps on inflation in European countries, which are not members of the
European Union. Island states with a high human development index were dealt with by
Assenmacher-Wesche, Gerlach and Sekine (2008) and Abbas and Sgro (2011). Michaelides
and Millos (2009) and Mohanty and John (2015) focused on two Asian countries, Russia,
and India. In addition to the output gap, these authors also included oil prices and the fiscal
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When Inflation Again Matters: Do Domestic
and Global Output Gaps Determine Inflation in the EU?
578 Amfiteatru Economic
deficit in the model. The determinants of Russian inflation are high oil prices, but also the
output gap (Michaelides and Millos, 2009). Andrei et al. (2022) enriched the existing
literature by highlighting the importance of input price indices in the agricultural sector.
According to Kohlscheen and Moessner (2022), the output gap positively affects inflation in
a panel that includes 35 countries. Similarly, to the authors mentioned, Jašová et al. (2020)
examined the relationship between inflation and the output gap in a panel of countries. Jašová
et al. (2020) focused on two panels. The first group consisted of emerging countries, and the
second included advanced countries. In addition to the domestic output gap, the authors also
included the global output gap in the model, with both output gaps representing the
determinant of inflation. The panel consisting of emerging countries was characterised by
the fact that after the crisis the impact of the global output gap on inflation declined and the
impact of the domestic output gap on inflation remained stable. Exactly the opposite is true
for the panel that includes advanced economies. The study by Manopimoke (2015) also
looked at the impact of output gaps on inflation in emerging and advanced economies. In
both panels, the determinant of inflation is the global output gap. The relationship between
this gap and inflation is related to the degree of trade openness (TO). Finally, Szafranek
(2021) pointed out that increased business cycle synchronisation explained strengthen price
co-movements within EU economies.
2. Data
Firstly, we applied the Granger causality test, which includes 24 EU countries (the EU-27
except for Malta, Croatia, and Bulgaria, which have been excluded from our sample due to
missing data). We used quarterly data from 1997 to 2020. The paper deals with the causality
between these pairs of variables: DOG and GOG, DOG and inflation, GOG and inflation,
NEER and inflation. Data are retrieved databases from Eurostat (2022a; 2022b; 2022c;
2022d; 2022e), the International Monetary Fund (2022a; 2022b; 2022c), and the World Bank
(2022a; 2022b; 2022c). Each time series is tested for stationarity using the Augmented
Dickey-Fuller (ADF) test, the Phillips-Perron (PP) test, and the Kwiatkowski-Phillips-
Schmidt-Shin (KPSS) test, similarly to the paper by Vyrostková and Mirdala (2022).
We used quarterly real gross domestic product (GDP) data to calculate the quarterly domestic
output gap. The real GDP was seasonally adjusted, we computed potential GDP using the
HP filter with smoothing parameter λ =1600, and the difference between real and potential
GDP represented the domestic output gap. In order to calculate the global output gap, we first
had to identify the main trading partners of the selected country. The main trading partners
are economies, whose export or import accounts for at least 2% of the total volume of export
or import (as Łyziak, 2019). We identified business partners based on data available from the
World Integrated Trade Solution (2022) (indicators: export partner share in % and import
partner share in %). Whereas the export or import of some economies did not account for at
least 2% of the total volume of the export or import each year, we calculated the arithmetic
average and identified all countries with an average of at least 2% as the main trading partners
of the analysed country.
According to Łyziak (2019) we calculated the global output gap using the following formula:
(1)
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Vol. 25 • No. 63 • May 2023 579
where:
k – the number of trading partners;
– the weight of trading partner n;
– the output gap in country n.
According to Borio and Filardo (2007), we calculated the weight of the trading partner n as
the sum of exports and imports of the trading partner to country j divided by the total sum of
exports and imports to country j.
In the second part of the paper, we estimate the panel data model for the EU-27. The annual
data cover the time period from 1999 to 2020. This period was divided into two shorter
periods: precrisis from 1999 to 2008 and postcrisis from 2009 to 2020.
Before analysing the determinants of inflation, we calculated the annual domestic and global
output gap. Real GDP per capita data (i.e., GDP data at constant prices) were used to calculate
the annual domestic output gap. The value of the smoothing parameter is λ =100. The annual
global output gap was calculated in the same way as the quarterly global output gap. We
calculated trade openness as the sum of exports and imports in % of GDP. The annual
nominal effective exchange rate is retrieved database from the Eurostat and unemployment
(U) from the International Monetary Fund. We verified the stationarity of the dependent
variable using unit root tests; data was stationary.
3. Research methodology
Using the Granger causality test and panel regression, we examined whether there is a
statistically significant relationship between the global output gap and inflation and between
the domestic output gap and inflation. The Granger causality test showed whether given
output gaps determine inflation with a lag. The determinants of inflation in the pre-crisis and
post-crisis periods were examined using panel regressions.
3.1. Granger causality testing
We were able to determine the direction of causality between variables using the Granger
causality test. We looked at the relationship between the DOG and inflation and the GOG
and inflation. The disadvantage of the Granger causality test is that it can only provide us
with information on the one-way or two-way relationship between variables, but it cannot
evaluate whether there is a positive or negative relationship between the variables.
The null hypothesis assumed that the variable "x" did not affect "y". The alternative
hypothesis assumed that the variable "x" affects "y". We also tested the opposite direction.
The null hypothesis assumed that the variable "y" did not affect "x". The alternative
hypothesis assumed that the variable "y" affects "x". If we do not reject the null hypotheses
at the chosen level of significance α = 0.05 in both equations, then the variables will be
independent (Baumöhl, 2009).
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When Inflation Again Matters: Do Domestic
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3.2. Panel data model
Using the panel data model, we examined the impact of selected variables on inflation within
the following groups of countries: European Union (27), Eurozone (19), and rather peripheral
countries of the EU (Greece, Cyprus, Lithuania, Latvia, Poland, Bulgaria, Croatia, Hungary,
Romania, Czech Republic, Estonia). A total of 12 models were created. We analyse the
impact of variables on inflation using adjusted equations:
Fixed effect model (FEM):
(2)
(3)
Random effect model (REM):
(4)
(5)
where:
– inflation in country i at time t;
– a fixed or random effect for countries or time periods;
– the domestic output gap in country i at time t;
– the global output gap in country i at time t;
– the nominal effective exchange rate in country i at time t;
– an unemployment in country i at time t;
– a trade openness in country i at time t.
We created a correlation matrix to find out if any of the variables should be excluded from
the model (if the correlation value was > 0.8) to avoid multicollinearity in these models.
The following tests are used to select the right type of panel model: the F-test or the Chow
test, the LM test and the Hausman test (see Table no.1). The resulting model must meet
certain assumptions. If any of the assumptions were not met, we solved this problem using
the variation-covariance matrix using the Arellano method.
Table no. 1. Choosing the right type of panel model
Test
Null hypothesis
Model selection after H0
rejection
F-test or Chow test
Pooled OLS is more convenient
than the Fixed effects model
(FEM)
FEM
Lagrange multiplier test
Pooled OLS is more convenient
than the Random effects model
(REM)
REM
Hausman test
The REM is more convenient than
the FEM
FEM
Source: Own representation according to Park, 2011.
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Vol. 25 • No. 63 • May 2023 581
4. Research results and discussion
As stated previously, we dealt with causality between variables within 24 European countries
(EU-27, except for Malta, Croatia, and Bulgaria). The analysis of inflation determinants
performed by panel regression included countries, which were divided into three groups:
i) members of the EU, ii) members of the Eurozone, and iii) peripheral countries of the EU.
The results of the Granger causality test, which includes the impact of output gaps on inflation
in the countries of the European Union, are presented in (Table no. 2.). Inflation was
determined by the domestic output gap in only six EU countries, namely Lithuania, Estonia,
Latvia, Finland, Italy, the Netherlands, and Lithuania. In Estonia and Lithuania, the domestic
output gap affected inflation with a lag of one, two, and three quarters. In the Netherlands,
inflation was influenced by the domestic output gap with lags of one, two, three, and four
quarters, in Finland and Latvia with lag of one quarter only, and in Italy with lag of three
quarters. The causality from the domestic output gap to inflation has been demonstrated in
more countries using one lag than using two or four lags. In Lithuania, the Netherlands, and
Estonia, inflation is determined by the domestic output gap at the 0.05 significance level
using a lag of one quarter.
The causality from the global output gap to inflation has not been confirmed in many EU
countries. In the Netherlands and Latvia, the global output gap influenced inflation with lag
of one, two, three, and four quarters. When we used a lag of two quarters, the number of
countries where inflation was affected by the global output gap increased. These countries
were Slovenia and Estonia.
Table no. 2. Granger causality testing: domestic output gap inflation; global output
gap inflation
Country
Causality
p-value
1 Lag
2 Lags
3 Lags
4 Lags
Estonia
DOG HICP
0.0009 ***
0.0196 *
0.0871 .
0.2704
GOG HICP
0.101
0.0413 *
0.8931
0.3568
Finland
DOG HICP
0.0566 .
0.1878
0.228
0.6119
GOG HICP
0.4444
0.2208
0.5375
0.913
Netherlands
DOG HICP
0.0002 ***
0.0015 **
0.005 **
0.0192 *
GOG HICP
0.0005 ***
0.0027 **
0.0075 **
0.04791 *
Italy
DOG HICP
0.1511
0.1726
0.0357 *
0.2409
GOG HICP
0.1732
0.2855
0.2647
0.6136
Latvia
DOG HICP
0.0974 .
0.1675
0.7259
0.9687
GOG HICP
0.0049 **
0.0064**
0.0427 *
0.0594 .
Lithuania
DOG HICP
0.0127 *
0.0162 *
0.0211 *
0.1558
GOG HICP
0.5115
0.4306
0.6622
0.7878
Slovenia
DOG HICP
0.5196
0.1637
0.1701
0.4624
GOG HICP
0.3351
0.0746 .
0.2752
0.5997
Note: ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level; DOG:
domestic output gap; GOG: global output gap; values are rounded to 4 decimal places.
We report the results of the Granger causality test, which includes the impact of inflation on
output gaps in the European Union countries, in (Table no. 3.). In Slovakia, the domestic
output gap is affected by inflation with a lag of four quarters, Germany is the only country
where the domestic output gap is affected by inflation with lags of one, two, three, and four
quarters. In Cyprus, Lithuania, Estonia, Germany, Slovakia, and the Netherlands, inflation
influences the domestic output gap using a lag by four quarters at the 0.05 significance level.
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When Inflation Again Matters: Do Domestic
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582 Amfiteatru Economic
The causality in the direction from inflation to the global output gap has been confirmed in
many EU countries using larger lags. In eight EU countries, using a lag of three quarters, we
confirmed the impact of the global output gap on inflation. Inflation was influenced by the
global output gap in Estonia, Latvia, Hungary, Ireland, Spain, Germany, Greece, the
Netherlands, Lithuania, and Portugal.
Table no. 3. Granger causality testing: inflation domestic output gap; inflation
global output gap
Country
Causality
p-value
1 Lag
2 Lags
3 Lags
4 Lags
Austria
HICP DOG
0.6395
0.9973
0.0321 *
0.0952 .
HICP GOG
0.2699
0.7486
0.6057
0.8317
Cyprus
HICP DOG
0.5153
0.1598
0.0618 .
0.04 *
HICP GOG
0.4032
0.4655
0.2542
0.3709
Germany
HICP DOG
0.0064 **
0.0038 **
0.0055 **
0.0246 *
HICP GOG
0.0035 **
0.0041 **
0.0021 **
0.0072**
Greece
HICP DOG
0.0582 .
0.1761
0.1184
0.1728
HICP GOG
0.9058
0.0074 **
0.0167 *
0.0255 *
Portugal
HICP DOG
0.5902
0.564
0.1434
0.298
HICP GOG
0.741
0.1063
0.0828 .
0.1494
Slovakia
HICP DOG
0.2468
0.515
0.3201
0.0105 *
HICP GOG
0.9817
0.5932
0.6149
0.8987
Belgium
HICP DOG
0.0873 .
0.592
0.5836
0.3542
HICP GOG
0.1488
0.4754
0.2947
0.4618
Hungary
HICP DOG
0.1557
0.2067
0.1222
0.1848
HICP GOG
0.0766 .
0.1942
0.3189
0.495
Ireland
HICP DOG
0.2306
0.1696
0.3224
0.1596
HICP GOG
0.9595
0.0425 *
0.0019 **
0.0088 **
Luxembourg
HICP DOG
0.0836 .
0.5017
0.6132
0.3082
HICP GOG
0.898
0.1227
0.1776
0.3044
Spain
HICP DOG
0.5555
0.4377
0.7537
0.7844
HICP GOG
0.4183
0.0743 .
0.1354
0.2081
Estonia
HICP DOG
0.6319
0.0008 ***
0.0010 **
0.0233 *
HICP GOG
0.0921 .
0.0151 *
0.0491 *
0.0906 .
Netherlands
HICP DOG
0.0573 .
0.2473
0.0102 *
0.0317 *
HICP GOG
0.1705
0.6246
0.0144 *
0.0479 *
Latvia
HICP DOG
0.2782
0.0859 .
0.1797
0.1046
HICP GOG
0.0824 .
0.0918 .
0.0101 *
0.03 *
Lithuania
HICP DOG
0.5204
0.0016 **
0.0024 **
0.0134 *
HICP GOG
0.3555
0.4078
0.0003 ***
0.0057 **
Slovenia
HICP DOG
0.9917
0.0995 .
0.2339
0.5631
HICP GOG
0.5646
0.119
0.2596
0.8855
Note: ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level; DOG:
domestic output gap; GOG: global output gap; values are rounded to 4 decimal places.
The results of the estimated panel models are displayed in (Table no. 4.). Panel 1 and Panel
2 cover the 27 countries of the European Union in the pre-crisis period i.e., from 1999 to
2008. The domestic output gap is added to the equation of the model Panel 1 as an
independent variable, while the global output gap is also included as an explanatory variable
but in the model Panel 2. The equations for Panel 3 and Panel 4 are different from Panel 1
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and Panel 2 only by the selection of periods, which in these cases is the post-crisis period,
i.e., 2009-2020. Inflation is determined as an explanatory variable in each model, and
inflation is expressed by the consumer price index (CPI). All four models are balanced and
have been adjusted using a variation-covariance matrix, due to unfulfilled assumptions. The
tables display the values of estimates considering individual and time effects. A random-
effects model was chosen for Panel 1 based on the Hausman test. In the pre-crisis period, the
domestic output gap does not affect inflation. On the other hand, the NEER is a variable that
has a significant impact on inflation. For Panel 2, a fixed effects model was chosen, and as
in the previous case, the NEER affects inflation. In the precrisis period, inflation in this panel
was determined by the global output gap, and its impact was positive.
For panels that consisted of EU countries but included the post-crisis period, a fixed-effects
model was chosen. The results differ from the previous two panels. In Panel 3, inflation is
determined by the domestic output gap, not by the NEER, and the effect of the output gap on
inflation is positive. Panel 4 is characterised by the fact that inflation is not determined by
any variable.
Table no. 4. Panel regression estimations: members of the European Union
Y = CPI
Pre-crisis period (1999-2008)
EU27
Post-crisis period (2009-2020)
EU27
Name of panel
PANEL 1
PANEL 2
PANEL 3
PANEL 4
Model Type
REM
FEM
FEM
FEM
Sample size
n=27, T=10, N=270
n=27, T=12, N=324
Estimate
Intercept
-19.6866904 ***
-
-
-
NEER
0.221946 ***
0.225073***
-0.0017910
-0.0045703
U
-0.242561
-0.053919
-0.0298181
-0.0571298
TO
-0.036479
-0.032753
-0.0061736
-0.0056583
DOG
-0.079492
does not enter
the model
0.1261731 .
does not enter
the model
GOG
does not enter the model
0.028061*
does not enter
the model
0.0042038
Note: ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level; p-value of
assumption tests (before modifying models): Breusch-Godfrey – Panel 1: < 2.2e-16, Panel 2: < 2.2e-
16, Panel 3: 5.174e-06, Panel 4: 4.967e-06, Pesaran CD test – Panel 1: 0.3181, Panel 2: 0.1143, Panel
3: 0.2215, Panel 4: 0.2295, Breusch-Pagan – Panel 1: 1.704e-07, Panel 2: 5.755e-07, Panel 3:
0.0006397, Panel 4: 0.001098; DOG: domestic output gap; GOG: global output gap; NEER: nominal
effective exchange rate; U: unemployment; TO: trade openness; REM: Random Effect Model; FEM:
Fixed Effect Model.
Secondly, we estimated for the Eurozone countries (Panel 5, Panel 6, Panel 7, and Panel 8)
and divided the time series into precrisis and postcrisis periods so that we could compare the
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determinants of inflation in different time laps (see Table no. 5.). Panel 5 and Panel 6 contain
190 observations, and Panel 7 and Panel 8 include 228 observations. According to Hausman's
test, a fixed-effects model was selected for each of the panels. All models are balanced. As
in the previous cases, inflation is the explanatory variable and is expressed through the CPI.
The domestic output gap was chosen as the independent variable for Panel 5 and Panel 7, and
the global output gap for Panel 6 and Panel 8. Panel 5 is characterised by the fact that inflation
in the precrisis period was determined by the domestic output gap and unemployment. A
negative statistically significant relationship, respectively, Phillips curve was confirmed
between unemployment and inflation. Inflation is positively influenced by the domestic
output gap. In the precrisis period, specifically in Panel 6, inflation in countries using the
euro is not affected by the global output gap. In the postcrisis period, the model results are
consistent in confirming the Phillips curve. In Panel 7, inflation was not affected by the
domestic output gap. In this panel, the only determinant of inflation is unemployment. We
confirmed the impact of the global output gap on inflation using a fixed-effects model in
Panel 8 (positive relationship). In this panel, the Phillips curve was also confirmed, i.e.
inflation is determined by unemployment (negative relationship).
Table no. 5. Panel regression estimations: members of the Eurozone
Y = CPI
Pre-crisis period (1999-2008)
Eurozone
Post-crisis period (2009-2020)
Eurozone
Name of panel
PANEL 5
PANEL 6
PANEL 7
PANEL 8
Model Type
FEM
FEM
FEM
FEM
Sample size
n=19, T=10, N=190
n=19, T=12, N=228
Estimate
Intercept
-
-
-
-
NEER
-0.110020
-0.1215409
0.10420627
0.1286238
U
-0.194136 .
-0.2812193 *
-0.09019873 .
-0.0830031 .
TO
-0.027074
-0.0245223
-0.00181307
-0.0022815
DOG
0.277703 .
does not enter
the model
0.00091931
does not enter
the model
GOG
does not enter
the model
0.0126005
does not enter
the model
0.0036462 .
Note: ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level; p- value of
assumption tests (before modifying models): Breusch-Godfrey – Panel 5: 4.124e-07, Panel 6: 2.618e-
07, Panel 7: 0.0008463, Panel 8: 0.0004075, Pesaran CD test – Panel 5: 0.4705, Panel 6: 0.4236, Panel
7: 0.1901, Panel 8: 0.1805, Breusch-Pagan – Panel 5: 0.0272, Panel 6: 0.01873, Panel 7: 0.0007275,
Panel 8: 0.0005572; DOG: domestic output gap; GOG: global output gap; NEER: nominal effective
exchange rate; U: unemployment; TO: trade openness; FEM: Fixed Effect Model.
We have chosen the following countries as peripheral countries of the EU: Greece, Cyprus,
Lithuania, Latvia, Poland, Belgium, Croatia, Hungary, Romania, the Czech Republic, and
Estonia. Using the Hausman test, we choose a random effects model for Panel 9 and Panel
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10 (precrisis period) and a fixed effects model for Panel 10 and Panel 11 (postcrisis period).
Panel 9 and Panel 11 included the domestic output gap. The global output gap was chosen as
the independent variable in Panel 10 and Panel 12. The other independent variables were the
same as in the previous models. All models were balanced and modified by the variation-
covariance matrix, due to the non-fulfilment of some assumptions. The number of
observations in both periods is lower than for panels covering the EU-27 or the Eurozone, as
this group of countries consists of only 11 countries. Panel 9 is in line with the result of Panel
1 (EU-27), i.e., in the pre-crisis period in Panel 9, only the NEER determines inflation, which
affects it positively. The Phillips curve is not confirmed in Panel 1 and in Panel 9. In this
period, inflation is also not affected by the domestic output gap. Panel 10 contains the largest
number of statistically significant variables, implying that we have confirmed the Phillips
curve for the peripheral EU countries in the precrisis period. In Panel 10, inflation is
positively affected by NEER and the global output gap, and inflation is negatively influenced
by unemployment (see Table no. 6.).
Table no. 6. Panel regression: The EU peripheral countries
Y = CPI
Pre-crisis period (1999-2008)
Peripheral countries of the EU
Post-crisis period (2009-2020)
Peripheral countries of the EU
Name of panel
PANEL 9
PANEL 10
PANEL 11
PANEL 12
Model Type
REM
REM
FEM
FEM
Sample size
n=11, T=10, N=110
n = 11, T = 12, N = 132
Estimate
Intercept
-15.762817 ***
-16.266718 ***
-
-
NEER
0.241407 ***
0.225786 ***
-0.0220084
-0.0240888
U
-0.463266
-0.357233 .
-0.0799858 *
-0.0894396 **
TO
-0.038014
-0.017890
-0.0088467
-0.0090380
DOG
0.502940
does not enter
the model
0.0434610
does not enter
the model
GOG
does not enter
the model
0.047585 **
does not enter
the model
0.0012092.
Note: ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level; p-value of
assumption tests (before modifying models): Breusch-Godfrey – Panel 9: 7.273e-07, Panel 10: 2.276e-
06, Panel 11: 0.08176, Panel 12: 0.07494, Pesaran CD test – Panel 9: 0.07908, Panel 10: 0.04949,
Panel 11: 0.01244, Panel12: 0.01285, Breusch-Pagan – Panel 9: 7.1e-05, Panel 10: 3.792e-05, Panel
11: 0.07317, Panel 12: 0.5669; DOG: domestic output gap; GOG: global output gap; NEER: nominal
effective exchange rate; U: unemployment; TO: trade openness; REM: Random Effect Model; FEM:
Fixed Effect Model.
We also confirmed similar findings by using cross-correlations and Vector Autoregressive
(VAR) models (see Table no. 7. to compare the results of different methods). The impact of
other determinants of inflation is shown in (Table no. 8.).
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Using the Granger causality test, we find that inflation is determined by the domestic output
gap rather than the global output gap in a larger number of countries. In the Netherlands,
Latvia, and Estonia, inflation is affected by both gaps. The VAR model was used to further
identify the relationships, again concluding that the domestic output gap is the determinant
of inflation in a large number of countries. In each of the countries studied, we confirmed a
positive relationship between DOG and inflation and a negative relationship between GOG
and inflation. The Portuguese and Estonian inflation is affected by both output gaps. The
global output gap has the highest ability to explain the variability of inflation in a larger
number of countries than the domestic output gap and affects inflation in the precrisis period
in two groups of countries: the EU and the EU peripheral countries. In the pre-crisis period,
the panel consisting of the peripheral countries of the European Union is characterised only
by the impact of the GOG on inflation. The DOG is the driving force behind inflation in the
Eurozone before the crisis and in the European Union after the crisis (see Table no.7.).
Table no. 7. Comparison of results of different methods
Method
The results
Granger causality
test
Determinant of inflation:
DOG: Estonia, Finland, the Netherlands, Lithuania, Latvia, Italy
GOG: Latvia, the Netherlands, Estonia, Slovenia
DOG+GOG: Estonia, the Netherlands, Latvia
VAR models
Determinant of inflation:
DOG: Luxembourg* (+), Greece* (+), Portugal* (+), Italy . (+),
Estonia . (+)
GOG: Portugal* (-), Estonia* (-)
DOG+GOG: Estonia . and Portugal*
Decomposition
of variability
and
IRF
Determinant of inflation: (we consider the tenth quarter)
DOG has the highest ability to explain inflation variability in: Greece
and Luxembourg.
GOG has the highest ability to explain inflation variability in: Estonia,
Italy, and Portugal.
In Greece and Luxembourg, inflation is responding positively to the DOG
shock. In Estonia, the inflation response to the impulse of the GOG is
negative. In Portugal, the inflation response to the shock is positive.
Cross correlations
Correlation between:
DOG and inflation: a slight correlation between DOG and inflation
was confirmed in Germany.
GOG and inflation: a slight correlation between GOG and inflation
was confirmed in Germany and Latvia.
Panel regression
Determinant of inflation in the pre-crisis period:
DOG: Eurozone . (+)
GOG: European Union* (+), the EU peripheral countries** (+)
Determinant of inflation in the post-crisis period:
DOG: European Union . (+)
GOG: Eurozone . (+)
Note: (+): positive relationship; (-): negative relationship; DOG: domestic output gap; GOG: global
output gap; IRF: Impulse response function, ***=0.001, **=0.01, *=0.05, .=0,1 indicate 0.1%, 1%,
5%, 10% significance level.
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Vol. 25 • No. 63 • May 2023 587
Using the Granger causality test, we find that NEER is a determinant of inflation in 10 EU
countries, and, on the other hand, using VAR models in 11 EU countries. The result matched
the results of the Granger causality test in Belgium, the Czech Republic, Estonia, Finland,
and Romania. Trade openness does not determine inflation in any panel in the pre-crisis or
post-crisis period. In the precrisis period, the NEER is a determinant of inflation in peripheral
EU countries and in the panel consisting of EU countries. In the post-crisis period, it has no
effect on inflation in any panel. In the EU countries, unemployment does not affect inflation
in either period but is a determinant of inflation in the Eurozone and the peripheral EU
countries (see Table no. 8.).
Table no. 8. Other determinants of inflation
Method
The results
Granger causality
test
Determinant of inflation:
NEER: Lithuania, Belgium, Hungary, Slovakia, Czech Republic,
Estonia, Finland, Italy, Ireland, Romania
VAR models
Determinant of inflation:
NEER: France **(-), Belgium* (-), Spain* (-), Romania** (+),
Austria*** (-),
Germany* (-), Denmark . (-), Finland** (-), Czech Republic** (-),
Poland*** (-), Estonia . (-)
Decomposition of
variability
and
IRF
Determinant of inflation: (we consider the second quarter)
NEER has the highest ability to explain inflation variability in
Estonia.
Inflation responds negatively to the nominal effective exchange rate
shock in Estonia.
Panel regression
Determinant of inflation in the pre-crisis period:
NEER: European Union ***(+), the EU peripheral countries***(+)
U: Eurozone . (PANEL 5) *(PANEL 6) (-), the EU peripheral
countries . (-)
Determinant of inflation in the post-crisis period:
NEER: is not the driving force behind inflation in any panel
U: Eurozone . (-), Peripheral countries of the EU *(PANEL 11),
**(PANEL 12) (-)
TO: is not the driving force behind inflation in any panel
Note: (+): positive relationship; (-): negative relationship; * Panel 10; NEER: nominal effective
exchange rate; U: unemployment; TO: trade openness; IRF: Impulse response function, ***=0.001,
**=0.01, *=0.05, .=0,1 indicate 0.1%, 1%, 5%, 10% significance level.
A comparison of the results with other studies is shown in (Table no. 9.). Busetti et al. (2021)
included NEER, GOG, and DOG (and other variables) in the model, which we also employed
in our research. The results of our study show that before the crisis the domestic output gap
affects inflation and after the crisis the global output gap. Unlike Busetti et al. (2021), we did
not define the NEER as a determinant of inflation. Jarociński and Lenza (2018) argue that in
countries that use the euro, DOG affects inflation after 2012 (the period analysed is from 3Q
2002 to 4Q 2015). Based on the results of our study, we argue that post-crisis GOG is a
determinant of inflation. Kendera (2015) confirmed the impact of DOG on inflation in
Slovakia using the VAR model, but we did not confirm this relationship. Using the Granger
causality test, the author demonstrated a statistically significant relationship between
inflation and DOG in other V4 countries. Tiwari, Oros and Albulescu (2014) examined the
impact of DOG on French inflation, and the results of their study show that there is a
significant relationship between these variables. Our results do not match because we only
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defined NEER as a determinant of French inflation. Similarly, to our study, the paper by
Gerlach and Svensson (2003) examined the impact of the output gap on inflation in the
Eurozone countries. According to our results, before the crisis, inflation was determined by
the output gap. According to the authors, it is appropriate to consider output gap when
judging price pressures. The results may be different due to the use of different econometric
methods, time periods, and the addition of other variables to the model.
Table no. 9. Confrontation of results
Note: other authors, such as Assenmacher-Wesche and Gerlach (2008), Michaelides and Millos (2009),
Abbas and Sgro (2011), Zhang and Murasawa (2011), Çiçek (2012), Valadkhani (2014), Mohanty and
John (2015), Jašová, et al. (2020) dealt with other countries (for instance: Australia, China, USA,
Canada, United Kingdom, Turkey, Russia, Switzerland); NEER: nominal effective exchange rate,
DOG: domestic output gap, GOG: global output gap.
Conclusions
The main aim of the contribution was to analyse the impact of domestic and global output
gaps on inflation. We used quarterly data from 1Q 1997 to 3Q 2020 and annual data divided
into two periods - precrisis from 1999 to 2008 and the postcrisis period from 2009 to 2020.
Annual data was used in panel regression models; quarterly data were used in the Granger
causality test. Before our analysis, we estimated the quarterly and annual domestic output
gap via the HP filter, and we calculated the quarterly and annual global output gap.
The results of our analysis, conducted via the Granger causality test, show that in Lithuania,
Estonia, the Netherlands, Finland, and Latvia, inflation is affected by the domestic output
gap with a lag by one quarter. In the Netherlands and Latvia, inflation is influenced by the
global output gap with a lag of one quarter. Both gaps are determinants of inflation with a
lag of one quarter only in the Netherlands and Latvia.
Author
Variable
Country
The impact of
the variable
Our results
Busetti et al.
(2021)
NEER
Eurozone
confirmed
unconfirmed
DOG
confirmed
confirmed (pre-crisis
period)
GOG
confirmed
confirmed (post-crisis
period)
Jarociński,
Lenza
(2018)
DOG
Eurozone
confirmed
unconfirmed (post-crisis
period)
Kendera
(2015)
DOG
Slovakia
confirmed
unconfirmed
Czech Republic
confirmed
unconfirmed
Hungary
confirmed
unconfirmed
Poland
confirmed
unconfirmed
Tiwari, Oros,
Albulescu
(2014)
DOG
France
confirmed
unconfirmed
Gerlach,
Svensson
(2003)
DOG
Eurozone
confirmed
confirmed (pre-crisis
period)
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Vol. 25 • No. 63 • May 2023 589
Through panel regressions, we find that precrisis inflation is affected by unemployment and
the domestic output gap, specifically in the panel that includes Eurozone countries. In the
EU, the global output gap and the NEER are determinants of inflation, and in the peripheral
EU countries, inflation is determined by the global output gap, unemployment, and the
NEER. After the crisis, variables such as trade openness and the nominal effective exchange
rate did not determine inflation in any group of countries. The global output gap and
unemployment were determinants of inflation in the group of countries using the euro. In the
EU countries, the domestic output gap influenced inflation, and in the peripheral EU
countries, unemployment affected inflation. Trade openness has not determined inflation in
any period. The impact of the domestic output gap on inflation strengthened in the EU
countries after the crisis, while it weakened in the euro area countries. The reverse was true
for the global output gap, but a weakening of the impact is also observed in the EU periphery
countries.
Regarding policy implications, the results of our study show that, contrary to common
practise, economic policy makers should take into account not only the domestic output gap
but also the global output gap in inflation forecasting models. According to our results,
inflation is affected by the global output gap in the Eurozone (post-crisis period) and in
individual EU countries. It seems that the current inflation crisis starting in 2022 is even more
sensitive to global effects, therefore, the global output gap cannot be neglected neither in
forecasting or policy decision process. A limitation of our study is the lack of focus on energy
shocks and oil prices. We did not deal with the topic of energy shock and oil price, because
of the current turbulent fluctuations in the markets. For this reason, the research results would
not be relevant. If longer time series are available, the topic will be suitable for future
research. In addition, it would be appropriate to examine determinants of inflation in Estonia
and other rather small EU countries, as their inflation (fragility of the country and sensitivity
to external shocks) is currently remarkably high.
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