1
NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
The Impact of Macroprudential Policy on Credit Growth in 
Nine Euro Area Economies
*
Eva Lorenčič
a
, Robert Volčjak
b
, Mejra Festić
c**
a
UBS Group AG, Bahnhofstrasse 45, 8001 Zürich, Switzerland
b
Bank of Slovenia, Slovenska 35, 1505 Ljubljana, Slovenia
c
University of Maribor, Faculty of Economics and Business, Razlagova 14, 2000 Maribor, Slovenia
eva.lorencic@ubs.com, robert.volcjak@guest.arnes.si, mejra.festic@um.si
 
ARTICLE INFO
 
Original Scientific Article
Article History:
Received October 2022
Revised January 2023
Accepted March 2023
 
JEL Classification:
E58, G28, E60, E44
 
Keywords:
Macroprudential policy
Systemic risk
Financial stability
Dynamic panel data
One-step system GMM
 
UDK: 336.77:338.1
DOI: 10.2478/ngoe-2023-0007
 
Cite this article as: Lorenčič, E., Volčjak, 
R., & Festić, M. (2023). The Impact of 
Macroprudential Policy on Credit Growth 
in Nine Euro Area Economies. Naše 
Gospodarstvo/Our Economy, 69(2), 1-14. 
DOI:  10.2478/ngoe-2023-0007.
 
©2022 The Authors. Published by 
Sciendo on behalf of University of 
Maribor, Faculty of Economics and 
Business, Slovenia. This is an open access 
article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/
by-nc-nd/4.0/). 
 
Abstract
In this paper, we investigate the impact of macroprudential policy measures 
(bundled together into a macroprudential policy index, MPI) on the non-
financial corporate sector credit and household credit growth using a one-step 
system GMM empirical research method. The goal of our paper is to test 
whether contractionary macroprudential policy stymies credit growth rate 
and whether expansionary macroprudential policy spurs credit growth rate 
in selected Euro Area economies (Austria, Belgium, Finland, Germany, Ireland, 
Italy, Netherlands, Slovenia, and Spain) over the period 2008Q4–2018Q4. We 
test two hypotheses: H1: The tightening of macroprudential policy measures 
reduces the non-financial corporate sector credit growth rate, and H2: The 
tightening of macroprudential policy measures reduces the growth rate of 
household credit. Based on our empirical results, we can confirm the first 
hypothesis. In contrast, the second hypothesis can be neither confirmed 
nor rejected since the explanatory variable of interest (MPI) is statistically 
insignificant in the second model.
Introduction
Banks' significant losses during the 2007–2008 subprime crisis called into 
question banks' risk-taking behavior. Lehman Brothers’ default pointed out 
that financial stability has a macroprudential or systemic dimension (Matysek-
Jędrych, 2018). If the financial system is treated simply as the sum of its 
parts, its historical tendency to transition between booms and busts can be 
overlooked (Beck & Gambacorta, 2020). Before the emergence of the crisis, 
banks were involved in exuberant risk-taking activities (Luu & Vo, 2021) and 
excessive lending to borrowers with dubious creditworthiness, which led to 
credit and asset price booms, a banking crisis, and a surge in non-performing 
loans (Festić & Romih, 2008). In the fallout of the crisis, policymakers and 
academics recognized that more effective macroprudential policies and 
regulatory actions are required to reduce excessive optimism of economic 
agents, stem moral hazard behavior, and prevent banks from unrestrained 
risk-taking (Luu & Vo, 2021). The “Greenspan doctrine” (Greenspan, 2002, 
*
 The views and opinions expressed in this paper are solely those of the authors and do not 
in any way reflect the official policy, position or opinion of UBS Group AG, Bank of Slovenia, 
or University of Maribor. The authors would like to thank the anonymous reviewers for their 
valuable remarks and suggestions.
** 
Corresponding author

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
2011), advocating the view that it was preferable to inject 
liquidity into the financial system after a final crisis had 
occurred, has ended. The ex-ante policy interventions 
are no longer seen as too costly, blunt, or unpredictable 
in their effects (Jeanne & Korinek, 2020). In this paper, 
we investigate the impact of macroprudential policy 
measures (bundled together into a macroprudential policy 
index, MPI) on the growth rate of loans extended by banks 
to non-financial institutions and households respectively. 
The objective of our paper is to establish whether 
contractionary macroprudential policy stymies credit 
growth rate and whether expansionary macroprudential 
policy spurs credit growth rate in selected Euro Area 
economies (Austria, Belgium, Finland, Germany, Ireland, 
Italy, Netherlands, Slovenia, and Spain) over the period 
2008Q4 to 2018Q4.
The first time the term “macroprudential” was used in an 
official report was in 1986 when the Cross Report was 
published (BIS, 1986; Bini Smaghi, 2009; Maes, 2010). 
In the Cross Report, the goal of the macro-prudential 
policy was defined as “the safety and soundness of broad 
financial system and payments mechanism”. The seminal 
papers by BIS economists which defined the concept of 
macroprudential policy are Borio (2003), Borio and White 
(2004), and White (2006). That said, while macroprudential 
tools may not have been actively used since the early 
1990s, they were frequently used and were an integral part 
of the policy toolkit of the Federal Reserve and of the other 
authorities in the United States between the First World 
War and the early 1990s (Elliott, Feldberg & Lehnert, 2013; 
Borio, 2011; ECB, 2020, Galati & Moessner, 2011). They 
were only not named “macroprudential”. Examples of such 
macroprudential tools used in the US are underwriting 
standards, stock margin requirements, selective credit 
controls on portfolios, reserve requirements, interest rate 
ceilings, capital requirements, supervisory guidance, and 
“direct pressure” etc. (Elliott, Feldberg & Lehnert, 2013). 
Nowadays, macroprudential policy is defined as “the use of 
primarily prudential tools to limit systemic risk – the risk 
of disruptions to the provision of financial services that 
is caused by an impairment of all or parts of the financial 
system, and can cause serious negative consequences for 
the real economy” (IMF, 2013).
Ben Bernanke, a former Federal Reserve Chairman, 
in 2008 acknowledged the need for macroprudential 
regulation by asserting: “Going forward, a critical question 
for regulators and supervisors is what their appropriate 
“field of vision” should be. Under our current system of 
safety-and-soundness regulation, supervisors often focus 
on the financial conditions of individual institutions in 
isolation. An alternative approach, which has been called 
systemwide or macroprudential oversight, would broaden 
the mandate of regulators and supervisors to encompass 
consideration of potential systemic risks and weaknesses 
as well” (Bernanke, 2008). Similarly, Andrew Crockett, 
a former general manager of the BIS, championed the 
push for macroprudential regulation with the following 
statement: “Strengthening the macro-prudential 
orientation of the regulatory and supervisory framework 
is important because of the costs and nature of financial 
instability. The main costs take the form of output losses. 
The nature of the processes generating instability puts a 
premium on a macro-prudential conception of economic 
behavior (Crockett, 2012).”
It strives to ensure that the financial system does not 
magnify a downturn in the real economy – for instance, by 
financial institutions having to reduce the supply of credit 
in a stressful situation (Aikman et al., 2019). The ultimate 
target of macroprudential policy is not to eliminate 
recessions altogether but rather to prevent the financial 
system from creating shocks that set off recessions and 
from magnifying shocks that make recessions worse 
(Aikman et al., 2019; ECB, 2020; Claessens, 2014). The 
key is the preventive, ex-ante reaction to the build-up of 
systemic risk. For instance, Laidroo and Männasoo (2014) 
demonstrate that regulatory and supervisory authorities 
should monitor credit lines for timely recognition of 
periods in which banks overextend credit. 
Systemic crises come about as a result of the 
build-up of financial imbalances (mostly leveraged 
booms) in the financial sector, which is why the 
ex-ante prevention of immoderate risk-taking is a key 
objective of macroprudential policy (Peydró, 2016). The 
macroprudential policy takes the edge off the credit 
supply cycles, which positively affects the real economy 
during a crisis (Peydró, 2016). While financial stability is 
the first and foremost goal of macroprudential policy, its 
intermediate target is the correction of externalities (e.g. 
excessive house price appreciation and credit growth) 
and the mitigation of market failures and imperfections 
which generate systemic risk even when microprudential 
supervision and monetary policy are conducted effectively 
(Claessens, 2014). First, the causes of systemic risk need 
to be identified, and, they need to be corrected by specific 
macroprudential tools and instruments. In our paper, the 
intermediate target of macroprudential policy measures 
implemented by individual Euro Area economies (bundled 
into a macroprudential policy index) is credit growth (the 
growth rate of loans extended by banks to non-financial 
institutions and households).

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
Even in 2014, more than seven years after the crisis, the 
meaning of the term “macroprudential regulation” was 
obscure (Barwell, 2013), and its effectiveness was debated 
(Galati & Moessner, 2014). There has been a spike in 
empirical and theoretical studies on macroprudential 
policy and macroprudential regulation in the past few 
years. Since 2008, the term “macroprudential” in speeches 
by central bankers has surged along with academic 
research on this topic (Galati & Moessner, 2011). 
Nonetheless, there is still insufficient agreement on what 
constitutes a macroprudential policy framework, which 
contrasts with a monetary policy framework where there 
is a clear consensus on the definition of an inflation-
targeting regime (Lombardi & Siklos, 2016). Our paper is a 
contribution to this field. 
This paper is structured as follows: The second section 
provides an overview of the theoretical background of 
the empirical analysis. The third section lays down data 
specification, hypotheses, and methodology. The fourth 
section sets out empirical results and discusses them. The 
fifth section concludes.
The paper aimed to test whether expansionary 
macroprudential policy spurs credit growth and vice versa. 
We selected the sample of countries on the criteria of 
compatible database and similar timelines of the impulses 
of macroprudential policy regarding to the economic cycle.
The implications for macroprudential policy conclude that 
the same approach in different countries has the same 
impact on credit growth regarding the economic cycle 
phase. The limitation of the study is the aggregate approach 
and general conclusion, which could differentiate between 
small and systemic important banks.
Theoretical Background
Granular credit registry data to study the impact of 
macroprudential policies has so far been used in very few 
cases: Dassatti Camors et al. (2019) investigate the impact 
of changes in reserve requirements in Uruguay; Jimenez 
et al. (2017) examine dynamic provisioning in Spain; and 
Gambacorta and Murcia (2020) use confidential bank-loan 
data to shed light on the effectiveness of macroprudential 
policy tools and their interaction with monetary policy (more 
in Claessens, 2014; Akinci and Olmstead-Rumsey, 2018; 
Poghosyan, 2020). 
Even in the euro area, the effectiveness of various monetary 
policy and macroprudential policy instruments varies and 
may or may not be in accordance with country-specific 
conditions, as demonstrated by Cocriş and Nucu (2014). 
In our paper, we use aggregate country-level data (as 
opposed to granular credit registry data) in line with the 
majority of the existing body of research on the effects of 
macroprudential policy.
The literature examining the impact of macroprudential 
policy instruments is vast and versatile (Morgan, Regis & 
Salike, 2019). In general, we can identify three strands of 
literature: The first strand is the empirical research employing 
cross-country macro data and assessing the impact of various 
macroprudential policy instruments on the housing market, 
the credit cycle, or some other indicator of financial stability 
(ibid., 2019). The second set is the case studies of countries 
using micro-level data and investigating the effect of one 
or more macroprudential policy instruments on financial 
stability (ibid., 2019). The third group of studies, which is the 
most recent one, employs both macro- and micro-level data 
to estimate the impact of country-specific macroprudential 
policy instruments on financial stability (ibid., 2019). This 
type of literature draws on the data retrieved from many 
banks headquartered in different countries, which gives an 
insight into how changes in macroprudential policy affect 
other countries and groups of banks (ibid., 2019).
Some studies assess the impact of macroprudential policy 
instruments on financial variables such as asset prices, 
credit, and economic imbalances in the economy (e.g., Akinci 
& Olmstead-Rumsey, 2018; Cerutti, Dagher & Dell’Ariccia, 
2015; Lim et al., 2011), whereas others focus on the impact 
of macroprudential policy instruments on macroeconomic 
variables traditionally targeted by monetary policy – inflation 
and output (e.g., Richter et al., 2019; Kim & Mehrotra, 2017). 
Most studies construct dummy indices that are based on the 
dates of policy actions (Lim et al., 2011; Shim et al., 2013; 
Cerutti, Claessens & Laeven 2017; Cerutti, Correa, Fiorentino 
& Segalla, 2017; Akinci & Olmstead-Rumsey, 2018). The 
dummy indices signal a tightening or a loosening of the 
macroprudential policy stance but do not reflect the intensity 
of changes in macroprudential policy instruments (Kim & 
Oh, 2020). Some relatively recent studies incorporate the 
power of macroprudential policy actions. For example, Alam 
et al. (2019) and Richter et al. (2019) created a loan-to-value 
(LTV) index reflecting the intensity of changes in the LTV 
cap. Vandenbussche et al. (2015) designed dummy indices of 
policy measures that incorporate the changes' intensity.
Recent empirical results indicate that debt-to-income 
and loan-to-value caps are more effective than capital 
requirements for limiting credit growth (Claessens et al., 
2013; Basten & Koch, 2015; Drehmann & Gambacorta, 
2012). Another strand of literature (e.g. Jakubik & Hermanek, 
2008) investigates the impact of macroprudential policy 

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
instruments on financial stability by constructing stress 
scenarios and presenting stress test results (more in Altunbas, 
Binici & Gambacorta, 2017).
Macroprudential policy instruments seem to be effective 
in mitigating the sensitivity of leverage and credit to the 
business cycle – i.e. the procyclicality of leverage and credit 
growth (Lim et al., 2011; Galati & Moessner, 2014; Claessens, 
2013). Macroprudential tools appear to be effective also 
in restraining asset growth, leverage, and credit growth 
(Vandenbussche et al., 2015; Alper et al., 2014; Cerutti, 
Claessens & Laeven, 2017; Claessens, Ghosh & Mihet, 2013; 
Olszak, Roszkowska & Kowalska, 2018). 
The empirical evaluation of the effectiveness of 
macroprudential policies is complicated for several reasons 
(Poghosyan, 2020): Insufficient number of macroprudential 
policy measures; intensity of measures; and endogeneity. 
These possible problems could potentially make our 
empirical assessment more complex and intricate.
Insufficient number of macroprudential policy measures: 
Certain macroprudential policy measures (such as liquidity 
coverage ratio, net stable funding ratio, and leverage ratio) 
have been enacted only 3-5 years ago (or even more recently 
or are obligatory only since end-June 2021 onwards) and in 
a limited number of countries, which may make the number 
of observations for the empirical assessment insufficient. 
Furthermore, it is difficult to estimate the dynamic effects 
of measures which came into force only recently because 
the number of observations on the target variable after 
the implementation of the measures is not yet sufficient 
(Poghosyan, 2020).
Intensity of measures: It is difficult to quantify the intensity of 
macroprudential policy measures. For example, an increase in 
the annual amortization requirement by 1% and a decrease in 
the loan-to-value (LTV) ratio by 5% indicate macroprudential 
tightening; however, it is difficult to say which of the two 
instruments is more stringent. Many researchers thus rely on 
categorical variables to differentiate between tightening and 
loosening calibration of macroprudential policy instruments 
(ibid., 2020). Carreras, Davis, and Piggott (2018) findings 
suggest that macroprudential policy instruments positively 
impact stalling household credit growth and house prices in 
both the short and long run. 
Endogeneity: The problem of reverse causality exists 
between macroprudential policy instruments and the 
target variables since the former are usually calibrated in 
response to a change in the latter. For example, in periods 
of rising house prices macroprudential policy will usually 
be tightened, leading to a positive correlation between 
the macroprudential policy variables and the residual 
(Vandenbussche et el., 2015). This can cause the coefficient 
of the macroprudential variable to be biased upwards. Hence, 
the estimated coefficients of macroprudential variables are 
usually given as lower bounds. If the regression coefficient of 
the macroprudential variable is insignificant and/or does not 
have the right sign, this can be a result of the upward bias. As 
a consequence, the estimation can be uncertain. Conversely, 
if the regression coefficient of the macroprudential variable 
has the right sign and is significant, the lower bound of the 
estimate is substantial, hence macroprudential policy can 
be characterized as effective. Many researchers resort to 
using the generalized method of movement (GMM) as the 
econometric method of choice for estimating parameters in 
statistical models (Poghosyan, 2020). In our paper, we employ 
the one-step GMM (dynamic panel-data estimation) empirical 
research method to avoid the problem of endogeneity.
Similarly, Olszak, Roszkowska and Kowalska (2019) investigate 
if macroprudential policies dampen the procyclical impact 
of capital ratios on bank lending in a sample of sixty-five 
countries. Of the investigated macroprudential instruments, 
only borrower-based measures such as LTV and DTI caps 
seem to countercyclically by weakening the positive impact 
of capital ratio on bank lending, particularly in crisis periods. 
Along the same lines, Arregui et al. (2013) develop an 
analytical framework for estimating macroprudential policies' 
costs, benefits, and unintended consequences. They propose 
a measure of net benefits of implementing macroprudential 
policy, composed of the probability of a crisis, the loss of 
output in a crisis, the ability of policy to reduce the likelihood 
and damage during a crisis, and the output costs of a 
certain policy decision. They also describe the unintended 
consequences of certain policies and identify instruments 
that could minimize such leakages. The macroprudential 
policy measures which authors identify as the most effective 
for stemming house price appreciation and credit growth 
are reserve requirements, higher risk weights on capital, and 
LTV limits. Loan loss provisioning policies do not seem to 
substantially affect house prices and credit. 
Comparably, Ma (2020) analyzes the trade-off between growth 
and financial stability as a consequence of macroprudential 
policy implementation. This is done by examining the effect of 
optimal macroprudential policy in a small open economy on 
growth and welfare (annual consumption). Macroprudential 
policy substantially strengthens financial stability (it reduces 
the frequency and probability of crises) at the cost of a very 
small negative effect on average growth and welfare. 
In the same vein, Akinci and Olmstead-Rumsey (2018) examine 
the effectiveness of macroprudential policies in limiting credit 

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
growth and house price growth by using a dynamic panel data 
model for 57 economies from 2000 to 2013. To this end, the 
authors develop new indices for seven macroprudential tools 
(LTV limits, DSTI limits, other housing measures, time-varying 
capital requirements, provision requirements, consumer loan 
limits, and credit growth ceilings). Counterfactual simulations 
indicate that, if the countries had not used any macroprudential 
policy measures in 2011–2013, the bank credit growth, 
housing credit growth, and house price appreciation would 
have been substantially higher. 
Similarly, Meuleman and Vander Vennet (2020) examine 
the effectiveness of macroprudential policy in mitigating 
the systemic risk of banks in Europe from 2000–2017. The 
systemic risk measure is decomposed into individual bank 
risk and systemic risk. This is crucial for the differentiation 
between microprudential and macroprudential policy effects. 
The macroprudential policy instruments seem to reduce 
individual bank risks and bank systemic risk, as assessed by 
stock market investors. 
Comparably, Altunbas, Binici, and Gambacorta (2017) shed 
light on the impact of macroprudential policies on bank 
risk by drawing on data from 61 countries over the time 
span 1990–2012. Small, weakly capitalized banks and 
banks having a high share of wholesale funding respond 
more strongly to changes in macroprudential policy tools. 
Macroprudential policies are more efficient when employed 
during a downturn than during a boom. 
Along the same lines, Zakaria and Fatine (2017) analyze 
macroprudential policy instruments and empirically show 
that these instruments should be deployed only in particular 
macroeconomic circumstances and with a certain risk profile 
of financial institutions. The variables to be considered when 
taking macroprudential policy decisions are the output gap 
(which depicts economic cycle), the Z-score, liquidity ratios, 
and changes in bank profitability. The use of macroprudential 
instruments mitigates the build-up of systemic risk in the 
financial system and positively affects its resilience. This 
notwithstanding, the use of financial instruments should be 
temporary so as not to lead to negative externalities. 
Similarly, Cizel, Frost, Houben and Wierts (2019) investigate 
whether implementing macroprudential policy leads to 
substituting bank credit with non-bank credit. However, 
the results vary across methodologies and samples. On the 
one hand, it could be claimed that the substitution effect 
leads to the propagation of new systemic risks. On the 
other hand, it could be asserted that the substitution effect 
reduces systemic risks since non-bank financial institutions 
are, by and large, less leveraged and with lower liquidity 
risks than the banks. 
Another comparable study is that of Bambulović and Valdec 
(2020) who investigate the impact of macroprudential 
policy measures on foreign and domestic banks’ lending in 
Croatia over a 19-year period split into the period before 
the 2008 financial crisis and after it. The study concludes 
that macroprudential policies were relatively successful in 
containing credit growth and constraining the build-up of 
risks for banks in foreign ownership. The macroprudential 
policy measures were more effective during the pre-
crisis period than during the crisis period. The direction of 
the measure implementation (tightening or loosening) 
does not result in an impact of the same magnitude. This 
resulted in an increase in lending activity in sectors other 
than the banking sector. Consequently, the private sector's 
indebtedness markedly increased. As such, one of the paper's 
conclusions is that policymakers should consider both the 
supply and the demand side of the borrowing and lending 
process. Furthermore, banks anticipated the introduction 
of macroprudential policy measures and increased their 
lending activities shortly beforehand, which underscores 
their procyclical behavior. Tighter macroprudential policy in 
the home countries of banks under foreign ownership had 
a negative impact on the lending of those banks in Croatia, 
which underlines regulatory spillover effects. This finding 
highlights the relevance of aligning policy stances and 
reciprocity agreements among the EU member states.
Along the same lines, Gambacorta and Murcia (2020) use 
confidential granular credit registry (bank-loan) data of 
five Latin American countries (Argentina, Brazil, Colombia, 
Mexico, and Peru) to investigate the effectiveness of 
macroprudential policy tools and their interaction with 
monetary policy. The panel regressions and meta-analysis 
technique are employed to compare results across countries. 
The key takeaways from the study are that macroprudential 
policies have been effective in stabilizing credit cycles 
and reining in banking sector risk; the policies aimed at 
restraining the credit cycle are more effective at curbing 
credit growth than policies aimed at enhancing financial 
institutions’ resilience; and macroprudential policy tools 
have a more pronounced impact on credit growth when the 
monetary policy complements them. 
In a similar vein, Ely, Tabak, and Teixeira (2021) use a novel 
identification approach based on the nearest neighbor 
matching with propensity scores and a system-GMM model 
to examine how twelve different macroprudential policy 
instruments impact the risk-taking behavior of banks 
by drawing on a sample of 16.255 banks in 45 emerging 
and developed countries and the time period 2000–2014. 
Empirical results show that instruments that attempt to 
mitigate vulnerabilities stemming from interconnectedness 
and contagion of the financial system (e.g., caps on asset 

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concentration and interbank exposures) reduce leverage, 
improve the tradeoff between the risk and return, and 
facilitate bank stability. Likewise, certain borrower-based 
instruments (e.g., loan-to-value ratio, debt-to-income ratio, 
and capital surcharges on systemically important banks) 
decrease leverage and positively impact bank stability. 
Concentration limits are more effective for bigger and 
more leveraged banks, whereas loan-to-value and debt-to-
income ratios are more efficient in concentrated markets. 
All structural and borrower-based policies appear less 
effective for more stable banks. Capital-based policies (e.g. 
countercyclical capital requirements, capital surcharges for 
systemically important banks, leverage ratios and dynamic 
loan-loss provisions) have mixed effects. Asset-based 
policies (e.g., caps on domestic and foreign currency loans) 
lead banks to reduce capital, which negatively affects bank 
stability. The effects of implementing various instruments 
are heterogeneous and differ depending on banks’ size, 
leverage, liquidity, risk level, and market concentration. 
The study results support the usage of macroprudential 
policy instruments in countries with very different market 
characteristics and institutional environments. 
Similarly, Cerutti, Claessens and Laeven (2017) shed light 
on the usage of macroprudential policy instruments in 119 
countries from 2000 to 2013 by drawing on the IMF data. The 
study results suggest that emerging market economies use 
macroprudential policy instruments most often – particularly 
those which influence the foreign exchange rate. Advanced 
economies seem to rely more on borrower-based policies. The 
imposition of a macroprudential policy instrument usually 
leads to a decline in household credit growth. Macroprudential 
policy effects are weaker in open, financially more developed 
countries. The implementation of macroprudential policy 
instruments tends to result in an increase in cross-border 
borrowing, which points at regulatory arbitrage if other 
countries do not reciprocate changes in policy instruments. 
Another empirical finding is that macroprudential policies 
are more effective during economic upturns and less effective 
during economic downturns. Overall, the results of the study 
suggest that macroprudential policies can substantially 
impact credit growth in the financial system. Furthermore, 
the effect on credit growth varies across instruments and 
countries.
Along the same lines, Olszak, Roszkowska and Kowalska 
(2018) investigate how effective several macroprudential 
policy instruments are in dampening the procyclicality of 
loan-loss provisions (LLPs) by drawing on individual bank 
information from more than 65 countries and by using the 
two-step GMM Blundell-Bond approach. The study results 
are three-fold: First, borrower-based macroprudential policy 
measures are more effective than other macroprudential 
policy instruments in dampening the procyclicality of loan-
loss provisions. Second, macroprudential policy instruments 
which are likewise effective in reducing the procyclicality of 
loan-loss provisions are dynamic provisions, large exposure 
concentration limits and taxes on specific assets. Third, debt-
to-income and loan-to-value caps are particularly effective in 
dampening the procyclicality of loan-loss provisions of large 
banks. For large banks, taxes and concentrations limits are 
likewise effective in reducing the procyclicality of loan-loss 
provisions. Dynamic provisions decrease the procyclicality of 
loan-loss provisions for banks of all sizes.
Data Specification, Hypotheses, and 
Methodology 
In this paper we use the following data (and variables 
created based on these data):
• Quarterly growth rate of household credit (source: Sta-
tistical Data Warehouse of the European Central Bank, 
henceforth ECB SDW, 2021);
• Quarterly growth rate of non-financial corporate sector 
credit (source: ECB SDW, 2021);
• Capital – solvency ratio, calculated as equity capital 
divided by total assets (source: ECB SDW, 2021);
• GDP growth rate (source: Eurostat, 2021);
• Unemployment rate (source: Eurostat, 2021);
• House price index – quarterly rate of change (source: 
Eurostat, 2021);
• Size – logarithm of total assets of all banking groups in 
an economy (source: ECB SDW, 2021);
• y2008 – a dummy variable which takes the value 1 if 
the year is equal to 2008; and value 0 if the year is 
different from 2008 (source: Own creation of a dummy 
variable);
• Crisis – a dummy variable which takes the value 1 if the 
year is equal to 2008 or 2009; and value 0 if the year is 
different from 2008 or 2009 (source: Own creation of a 
dummy variable);
• MPI (macroprudential policy index) – takes the value of 
-1, 0 or 1. If the sum of macroprudential policy action 
indicators for 17 macroprudential policy instruments is 
positive, the MPI index takes the value 1; if the sum is 
negative, the MPI index takes the value -1; if there are 
no policy actions in a given quarter or if they offset each 
other, the MPI index takes the value 0. Each tightening 
event is coded as a +1, each loosening event is coded as 
a -1, and no or neutral action is coded as a zero (source: 

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Restrictions on foreign currency lending are mostly 
captured in "LFC" .
• LFC – limits on foreign currency: Limits on foreign 
currency (FC) lending, and rules or recommendations 
on FC loans.
• LTV – limits on the loan-to-value ratio: Limits to the 
loan-to-value ratios, including those mostly targeted 
at housing loans, but also includes those targeted at 
automobile loans, and commercial real estate loans.
• DSTI – limits to the debt-service-to-income ratio: 
Limits to the debt-service-to-income ratio and the 
loan-to-income ratio restrict the size of debt services 
or debt relative to income. They include those targeted 
at housing loans, consumer loans, and commercial real 
estate loans.
• Tax measures: Taxes and levies applied to specified 
transactions, assets, or liabilities, which include stamp 
duties, and capital gain taxes.
• Liquidity requirements: Measures taken to mitigate 
systemic liquidity and funding risks, including minimum 
requirements for liquidity coverage ratios, liquid asset 
ratios, net stable funding ratios, core funding ratios 
and external debt restrictions that do not distinguish 
currencies.
• LTD – Limits on the loan-to-deposit ratio: Limits to the 
loan-to-deposit (LTD) ratio and penalties for high LTD 
ratios.
• LFX – Limits on foreign exchange positions: Limits 
on net or gross open foreign exchange (FX) positions, 
limits on FX exposures and FX funding, and currency 
mismatch regulations.
• RR – Reserve requirements: Reserve requirements 
(domestic or foreign currency) for macroprudential 
purposes. Please note that this category may currently 
include those for monetary policy as distinguishing 
those for macroprudential or monetary policy purposes 
is often not clear-cut. A subcategory of reserve require-
ments is provided for those differentiated by currency 
(RR_FCD), as they are typically used for macropruden-
tial purposes.
• SiFi – Measures taken to mitigate risks from global and 
domestic systemically important financial institutions 
(SIFIs), which include capital and liquidity surcharges.
• Other: Macroprudential measures not captured in the 
above categories—e.g., stress testing, restrictions on 
profit distribution, and structural measures (e.g., limits 
on exposures between financial institutions).
The data are used for nine Euro Area economies (Austria, 
Belgium, Finland, Germany, Ireland, Italy, Netherlands, 
Integrated Macroprudential Policy (iMaPP) of the Inter-
national Monetary Fund; henceforth IMF iMaPP, 2021).
MPI (macroprudential policy index) is a sum of 
loosening and tightening measures of the following 17 
macroprudential policy instruments in a particular country 
in a specific quarter (IMF iMaPP, 2021; Zohair et al., 2019):
• Countercyclical capital buffer
1
: Banks must maintain 
a countercyclical capital buffer. Implementations at 
0% are not considered as a tightening in dummy-type 
indicators.
• Capital conservation buffer: Requirements for banks to 
maintain a capital conservation buffer, including the 
one established under Basel III.
• Capital requirements: Capital requirements for banks, 
which include risk weights, systemic risk buffers, and 
minimum capital requirements. Countercyclical capital 
buffers and capital conservation buffers are captured 
in their sheets respectively and thus not included here. 
Subcategories of capital measures are also provided 
in separate sheets, classifying them into household 
sector targeted (Capital_HH), corporate sector targeted 
(Capital_Corp), broad-based (Capital_Gen), and FX-loan 
targeted (Capital_FX) measures.
• LVR – leverage limits: A limit on leverage of banks, cal-
culated by dividing a measure of capital by the bank’s 
non-risk-weighted exposures (e.g., Basel III leverage 
ratio).
• LLP – loan loss provisions: Loan loss provision require-
ments for macroprudential purposes include dynamic 
provisioning and sectoral provisions (e.g., housing 
loans).
• LCG – limits on credit growth: Limits on growth or 
the volume of aggregate credit, the household-sector 
credit, or the corporate-sector credit by banks, and pen-
alties for high credit growth. Subcategories of limits to 
credit growth are also provided, classifying them into 
household sector targeted (LCG_HH), corporate sector 
targeted (LCG_Corp), and broad-based (Gen) measures.
• Loan restrictions: Loan restrictions are more tailored 
than those captured in "LCG". They include loan limits 
and prohibitions, which may be conditioned on loan 
characteristics (e.g., the maturity, the size, the LTV ratio 
and the type of interest rate of loans), bank character-
istics (e.g., mortgage banks), and other factors. Subcat-
egories of loan restrictions are also provided, classify-
ing them into household sector targeted (LoanR_HH), 
and corporate sector targeted (LoanR_Corp) measures. 
1
 Klinger and Teply (2014) demonstrate that sufficient capital buffers 
are key for safeguarding the stability of the financial system as a whole.

8
NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
Slovenia, and Spain) over the time span 2008Q4 to 2018Q4.
We test the following two hypotheses:
H
1
: The tightening of macroprudential policy measures 
reduces non-financial corporate sector credit growth 
rate.
H
2
: The tightening of macroprudential policy measures 
reduces the growth rate of household credit.
In the first model (to which the first hypothesis applies), 
two explanatory variables are used: Capital and MPI. The 
dependent variable is the growth rate of non-financial 
corporate sector credit (henceforth NFIGR). We assume that 
Capital will have a positive impact on NFIGR, since banks 
which are better capitalized are better able to absorb 
losses (and hence may be able to extend loans to riskier 
clients); satisfy regulatory capital requirements (and hence 
do not face any supervisory restrictions for extension of 
loans) and are overall in a better position to extend loans 
and expand their scope of business activities. We expect 
that MPI
2
 will have a negative effect on NFIGR, since the 
tightening of macroprudential policy measures should 
impose direct and indirect limits on banks’ credit activity. 
In the second model (to which the second hypothesis 
applies), three explanatory variables are used: Size, MPI, 
and Unemployment. The dependent variable is the growth 
rate of household credit (henceforth HHGR). We postulate 
that Size (logarithm of total assets of all banking groups 
in an economy) will have a positive impact on HHGR, 
since in a bigger banking sector, there should be more 
interdependencies among banks, the banking sector 
should be more important relative to other sectors, and the 
households may be more dependent on banks to satisfy 
their credit needs. We expect that MPI will have a negative 
effect on NFIGR, since the tightening of macroprudential 
policy measures should impose direct and indirect limits 
on banks’ credit activity. We presume that Unemployment 
will have a negative impact on NFIGR, since a higher 
unemployment rate indicates that a greater proportion of 
the population has a lower (or no) income; there is greater 
uncertainty in the economy; the employed part of the 
population might be more concerned about losing their 
job and might therefore be less willing and/or less able 
to take out a loan (depending on their job security). Table 
1 summarizes our expectations concerning the sign of the 
regression coefficients.
2
 The more macroprudential policy measures were tightened in a par-
ticular quarter, the higher (more positive) value the MPI has.
Table 1
Expected signs of regression coefficients
Explanatory variable
Expected sign of regression 
coefficient
Capital +
MPI -
Size +
Unemployment -
To test our hypotheses, we use the one-step system 
generalized method of movements (GMM), an empirical 
research method used for dynamic panel-data estimation. 
GMM is a dynamic panel data estimator and a generic 
method for estimating parameters in statistical models. It 
uses moment conditions which are functions of the data 
and the model parameters such that their expectation is 
equal to 0 at the parameters’ true value (Roodman, 2009, 
2014; Mileva, 2007). GMM controls for correlation between 
the explanatory variable and the error term in a model 
(i.e. for the endogeneity of the lagged dependent variable 
in a dynamic panel model). Furthermore, it controls for 
omitted variables bias, unobserved panel heterogeneity, 
and measurement errors (Roodman, 2009, 2014; Mileva, 
2007). Hence, GMM is suitable for use in settings 
characterized by independent variables which are not 
strictly exogenous (but are correlated with the error term); 
arbitrarily distributed fixed effects, heteroscedasticity, and 
autocorrelation within groups or panels.
In GMM models, the number of groups or cross-sections 
(N) must exceed the time span (T). Instrumental variables 
(IV) are used in the model. The instruments (Z) must be 
exogenous (E(Z^' u)=0). The number of instruments (Z) 
must be lower than or equal to the number of groups (N). 
There are two sets of GMM estimators: Difference GMM and 
system GMM. They were developed by Holtz-Eakin, Newey, 
and Rosen (1988); Arellano and Bond (1991); Arellano and 
Bover (1995); and Blundell and Bond (1998).
Difference GMM, proposed by Arellano and Bond (1991), 
corrects endogeneity in the model by transforming all 
regressors through differencing and removing fixed effects. 
However, the disadvantage of the first difference GMM is 
that it subtracts the previous observation from the current 
one, thereby increasing the gaps in an unbalanced panel. 
System GMM, developed by Arellano and Bover (1995) 
and Blundell and Bond (1998), corrects endogeneity in 

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
the model by introducing more instruments to improve 
efficiency and by transforming the instruments to make 
them uncorrelated (exogenous) with fixed effects. It creates 
a system of two equations: The original and transformed. 
It uses orthogonal deviations: The average of all future 
available observations of a variable is subtracted from the 
current observation. Regardless of the number of gaps in 
the data, this can be calculated for all observations apart 
from the last one, which minimizes data loss.
System GMM is an augmented estimator: One equation 
is expressed in levels form with first differences used as 
instruments. The second equation is expressed in first 
differenced form with levels as instruments. One-step 
system GMM is simply an augmented version of the 
one-step difference GMM. It uses more moment conditions 
than the one-step difference GMM. Moreover, it is efficient 
and robust to autocorrelation and heteroscedasticity 
(Roodman, 2009, 2014; Mileva, 2007). To implement the 
system GMM in Stata, we used the “xtabond2” command. 
We did not use the “small” option in Stata; hence the 
z-statistics/Wald statistics were reported (instead of 
t-statistics/F-statistics).
Empirical Results and Discussion
In this section, we present the results of two models: One 
with the quarterly growth rate of non-financial corporate 
sector credit as the dependent variable (“NFI model”) and 
one with the quarterly growth rate of household credit as 
the dependent variable (“HH model”). Both models were 
estimated with one-step system GMM. Both models are 
applied to nine Euro Area economies (Austria, Belgium, 
Finland, Germany, Ireland, Italy, Netherlands, Slovenia, and 
Spain) and to time period 2008Q4–2018Q4. 
There are two tests for the validity of instruments: Hansen's 
(1982) J test and Sargan's (1958) test for the validity of 
over-identifying restrictions. The null hypothesis (H
0
) is: 
“The instruments used are valid.” The alternative hypothesis 
(H
1
) is: “The instruments used are not valid.” Not rejecting 
H
0
 (i.e. “accepting” H
0
; p > 0.1) supports the choice of the 
instruments. The most favorable values of the Hansen and 
Sargan tests are between 0.1 and 0.6. However, values up 
to 0.9 are still acceptable, whereas values exceeding 0.9 
indicate that the model may be misspecified (Roodman, 
2009).
Moreover, there is a test for serial correlation and 
autocorrelation of the error term: The null hypothesis (H
0
) 
is: “The differenced error term is not first order (AR(1)) 
serially correlated.” The alternative hypothesis (H
1
) is: “The 
differenced error term is first order (AR(1)) serially correlated.” 
For the second order serial correlation, the hypotheses are 
comparable, only AR(1) is replaced by AR(2). Not rejecting 
H
0
 (i.e. “accepting” H
0
; p > 0.1) means that the error term is 
serially uncorrelated and that the moment conditions are 
correctly specified.
Three instruments are used in the NFI model: GDP growth 
rate; unemployment rate; and house price index. The number 
of instruments (3) is less than the number of groups (9). The 
NFI model uses two explanatory variables: Capital L1 and 
MPI L2. 
The explicit form of the model is (level equation):
yyX
it it it ii t ,, ,
'
,
 

 
1 (1)
differenced equation:
yy yy
XX
it it it it
it it it i
,, ,,
,, ,
()
() (
  
 
 

1011 2
21

 
,, )
t1
(2)
where:
y
X
it
it
,
,


endogenous variable
strictly exogenous covariates
 

i
it


the panel-level effects
i.i.d. over the whole samp
,
l le
estimation parameters 
i

Table 2
Results of the one-step system GMM estimation for the NFI model
Statistics NFI model
Wald chi2(2) 8.38 (0.015)**
Capital L1 0.012831 (0.006)***
MPI L2 -0.1274296 (0.063)*
Constant -0.196045 (0.004)***
AR(1) -2.02 (0.043)**
AR(2) 0.71 (0.479)
Sargan chi2(1) 0.19 (0.666)
Notes: “L1” denotes one lag, whereas “L2” indicates two lags. The 
z-/chi2-statistics are given in brackets below the coefficients and 
the p-values are given in brackets below the z-/chi2-statistics. 
Significance levels are denoted as: ***Significant at 1%. **Significant 
at 5%. *Significant at 10%.

10
NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
In the NFI model, the explanatory variables Capital L1 
and MPI L2 are statistically significant at 1% and 10%, 
respectively. The constant is likewise statistically significant 
at 1%. The model as a whole is statistically significant 
at 5%. The p value of the Arellano-Bond test for AR(1) in 
first differences is equal to 0.043, which indicates that the 
differenced error term could be first order serially correlated 
at 5%. However, the p value of the Arellano-Bond test for 
AR(2) in first differences exceeds 0.1, which indicates that 
the differenced error term is not second order (AR(2)) 
serially correlated. Hence, the p value for AR(1) being less 
than 0.05 is not a problem. The p value of the Sargan test is 
greater than 0.1 and less than 0.9, implying that we cannot 
reject H0, hence it can be concluded that the instruments 
used are valid. Both explanatory variables (Capital L1 and 
MPI L2) have the expected signs of regression coefficients 
(Capital: a positive sign and MPI a negative sign). Since the 
regression coefficient of MPI is negative, the first hypothesis 
(H1: Tightening of macroprudential policy measures reduces 
the growth rate of non-financial corporate sector credit.) can 
be confirmed.
Four instruments are used in the HH model: GDP growth 
rate; house price index; y2008; and Crisis. The number of 
instruments (4) is less than the number of groups (9). The 
HH model uses three explanatory variables: Size L1; MPI L1; 
and Unemployment L1.
Table 3
Results of the one-step system GMM estimation for the HH model
Statistics HH model
Wald chi2(3) 17.62 (0.001)***
Size L1 -0.0160128 (0.040)**
MPI L1 0.0173952 (0.565)
Unemployment L1 -0.0031554 (0.093)*
Constant 0.1232222 (0.007)***
AR(1) -1.43 (0.153)
AR(2) -0.84 (0.399)
Sargan chi2(1) 0.51 (0.474)
Hansen chi2(1) 0.53 (0.465)
Notes: “L1” denotes one lag, whereas “L2” indicates two lags. The 
z-/chi2-statistics are given in brackets below the coefficients and 
the p-values are given in brackets below the z-/chi2-statistics. 
Significance levels are denoted as: ***Significant at 1%. **Significant 
at 5%. *Significant at 10%.
In the HH model, the explanatory variables Size L1 and 
Unemployment L1 are statistically significant at 5% and 10%, 
respectively. The constant is likewise statistically significant 
at 1%. On the other hand, the explanatory variable MPI L1 
is not statistically significant, which implies that it is not 
possible to confirm or reject our second hypothesis (H2: 
Tightening of macroprudential policy measures reduces the 
growth rate of household credit.) based on the available 
data and results. The model as a whole is statistically 
significant at 1%. The p value of Arellano-Bond test for AR(1) 
and for AR(2) in first differences exceeds 0.1, which indicates 
that the differenced error term is neither first order (AR(1)) 
nor second order (AR(2)) serially correlated. The p value of 
the Sargan and Hansen tests is greater than 0.1 and less 
than 0.5, implying that we cannot reject H
0
, hence it can 
be concluded that the instruments used are valid. Only one 
explanatory variable (Unemployment L1) has the expected 
sign of regression coefficient (a negative sign).
Conclusion
The aim of macroprudential policy, tools, instruments and 
measures is to build up (capital and liquidity) buffers in 
expansionary periods such that they can be drawn down 
in periods of financial distress. This dampens the pro-
cyclicality
3
 of the financial system, which in turn improves 
financial stability (Borio, 2011). The macroprudential policy 
objective is to prevent systemic risk from taking shape 
and unfurling in the financial system, and hence to reduce 
the probability of financial crises with significant output 
losses for the economy as a whole. By identifying and 
restraining channels of formation and spread of systemic 
risk, macroprudential policy acts preventively against any 
signs of financial instability and mitigates their impacts if 
preventative measures falter (Frait & Komarkova, 2011). In 
the post-crisis era, interconnectedness among banks and 
sovereigns has declined, albeit unevenly across the euro 
area countries. Moreover, the institutional reforms intending 
to cut the bank-sovereign nexus are incomplete (Stawasz-
Grabowska, 2020). Since the macroprudential policy came 
to the forefront of the economic profession only recently, 
evidence on the effectiveness of macroprudential tools is still 
scarce. Our paper is a contribution to this field.
According to Mérö (2017), macroprudential targets are slightly 
ambiguous (decreasing systemic risk versus increasing 
macroprudential shock-absorbing capacity of banks); we do 
not yet know or have evidence if the new macroprudential 
rules are suitably calibrated; if the usage of new instruments 
amplifies the possibilities for regulatory arbitrage; what are 
the interactions between macroprudential and monetary 
policy; and if the usage of macroprudential tools can create 
certain risks – for instance, those which arise from economic 
agents increasingly resorting to the use of unregulated 
shadow banking that is (currently) outside the purview 
of macroprudential legislation. Our paper contributes to 
3
 Pro-cyclicality is defined as the inclination of the financial system to 
reinforce the business cycle (Festić, 2006).

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (2) 2023 Lorenčič, E., Volčjak, R., Festić M.
investigating the effectiveness of macroprudential policy 
measures and hence to closing some of the existing gaps in 
the economic scientific community.
We tested two hypotheses: H
1
: The tightening of 
macroprudential policy measures reduces non-financial 
corporate sector credit growth rate. H
2
: The tightening of 
macroprudential policy measures reduces the growth rate 
of household credit. Based on our empirical results, we can 
confirm the first hypothesis. In contrast, the second hypothesis 
can be neither confirmed nor rejected, since the explanatory 
variables of interest (MPI) is statistically insignificant. In the 
NFI model where quarterly growth rate of non-financial 
corporate sector credit is employed as the dependent 
variable, the explanatory variable MPI has the expected 
sign (negative) and is statistically significant. As such, it 
can be concluded that macroprudential policy measures do 
play an important role in stymying non-financial corporate 
sector credit growth, and, by extension, in cooling down 
the economy, and safeguarding financial stability. On the 
other hand, in the HH model where quarterly growth rate of 
household credit is employed as the dependent variable, the 
explanatory variable MPI does not have the expected sign 
and is not statistically significant. Thus, no conclusion can be 
drawn about the impact of macroprudential policy measures 
on household credit growth rate.
This paper was taking its final shape in 2021, a year marked 
by COVID-19 and its economic downturn (Nakatani, 2020). 
Macroprudential policy measures and capital controls can be 
used during the coronavirus turmoil to help prevent economic 
crisis from transitioning into a financial crisis (ibid., 2020). 
There are concerns that emerging and developing economies 
could experience substantial capital outflows which may 
cause liquidity problems in domestic or foreign currencies in 
the banking and corporate sectors, particularly in economies 
where currency mismatches and exchange rate depreciations 
are widespread (ibid., 2020). The coronavirus crisis could also 
adversely affect the real estate sector and lead to a decline 
in asset prices (Nakatani, 2020). While we do not examine the 
use and effectiveness of macroprudential policy measures in 
2020 and 2021 (because the data which would be required 
for an empirical analysis were not available), this period is 
likely to be extensively studied in the future.
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Vpliv makroprevidnostne politike na rast posojil v devetih 
gospodarstvih evroobmočja
Izvleček
V članku raziskujemo vpliv ukrepov (ki so združeni v indeks makroprevidnostne politike, MPI) makroprevidnostne politike na 
stopnjo rasti kreditov, danih sektorju nefinančnih podjetij in sektorju gospodinjstev, z uporabo empirične raziskovalne metode 
enokoračnega sistema GMM. Cilj pričujočega članka je preveriti, ali kontrakcijska makroprevidnostna politika zmanjša stopnjo 
kreditne rasti in ali ekspanzivna makroprevidnostna politika spodbudi stopnjo kreditne rasti v izbranih državah evroobmočja (v 
Avstriji, Belgiji, Finski, Nemčiji, Irski, Italiji, Nizozemski, Sloveniji in Španiji) v časovnem obdobju od 2008Q4 do 2018Q4. Preverimo 
dve hipotezi: H1: Poostritev ukrepov makroprevidnostne politike zmanjša stopnjo kreditne rasti v sektorju nefinančnih podjetij 
in H2: Poostritev ukrepov makroprevidnostne politike zmanjša stopnjo kreditne rasti v sektorju gospodinjstev. Na osnovi naših 
empiričnih rezultatov lahko potrdimo prvo hipotezo. Po drugi strani pa druge hipoteze ne moremo niti potrditi niti zavrniti, saj 
pojasnjevalna spremenljivka MPI, ki nas zanima, ni statistično značilna v našem drugem modelu.
Ključne besede: makroprevidnostna politika, sistemsko tveganje, finančna stabilnost, dinamični panelni podatki, enostopenjski 
sistem GMM