Fiscal-monetary-financial stability interactions in a data-rich environment

Review of Economic Perspectives – Národohospodářský obzor Vol. 18, Issue 3, 2018, pp. 195–223, DOI: 10.2478/revecp-2018-0012 © 2018 by the authors; licensee Review of Economic Perspectives / Národohospodářský obzor, Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 3.0 license, Attribution – Non Commercial – No Derivatives. Fiscal-Monetary-Financial Stability Interactions in a Data-Rich Environment Martin Hodula, 1 Lukáš Pfeifer 2 Abstract: In this paper, we shed some light on the mutual interplay of economic policy and the financial stability objective. We contribute to the intense discussion regarding the influence of fiscal and monetary policy measures on the real economy and the financial sector. We apply a factor-augmented vector autoregression model to Czech macroeconomic data and model the policy interactions in a data-rich environment. Our findings can be summarized in three main points: First, loose economic policies (especially monetary policy) may translate into a more stable financial sector, albeit only in the short term. In the medium term, an expansion-focused mix of monetary and fiscal policy may contribute to systemic risk accumulation, by substantially increasing credit dynamics and house prices. Second, we find that fiscal and monetary policy impact the financial sector in differential magnitudes and time horizons. And third, we confirm that systemic risk materialization might cause significant output losses and deterioration of public finances, trigger deflationary pressures, and increase the debt service ratio. Overall, our findings provide some empirical support for countercyclical fiscal and monetary policies. Key words: financial stability, fiscal policy, macroprudential policy, monetary policy, interactions, policy mix JEL Classification: E44, E61, G28 Received: 20 March 2018 / Accepted: 24 May 2018 / Sent for Publication: 3 September 2018 Introduction Successful implementation of macroprudential policies requires, among other things, a good understanding of the interplay between economic policies, their respective targets, and financial sector development. In this context, there is an intense policy debate focused on determining the extent to which fiscal and monetary policy measures may influence the functioning of the financial sector (i.e. the financial cycle). This debate has generated interest in analysing the quantitative importance of policy transmission channels to the financial sector, their mutual coordination, and the implementation of macroprudential policy tools in smoothing the financial cycle. 1 VŠB-Technical University of Ostrava, Economic Faculty, Department of Economics, 15/2172 17. Listopadu St., 708 33 Ostrava, Czech Republic, e-mail: [email protected] 2 University of Economics in Prague, Department of Monetary Theory and Policy, 1938/4 W. Churchill Sq., 130 67 Prague 3, Czech Republic, e-mail: [email protected]z Review of Economic Perspectives 196 In this paper, we contribute to that debate by presenting comprehensive time-series evidence on the fiscal-monetary-macroprudential policy interactions in a data-rich environment. We differ from the rapidly expanding body of literature on monetary/fiscal policy and macroprudential policy interactions mainly in that we consider both policy shocks simultaneously and identify a specific policy mix that might increase systemic risk in the financial sector. Further, we also consider the kick-back effect of financial instability and analyze its propagation in the real economy. In the process, we aim to answer the following questions: First, does loose economic policy benefit financial stability? Second, do fiscal and monetary policy shocks impact the financial sector differently? And third, how do financial imbalances influence real economic activity? The analysis is conducted for the Czech Republic, a small open economy that went through a transformation process from a centrally-planned to a market-based economy in the 1990s. The selection of the country is purely pragmatic; the Czech Republic has a bankbased financial sector dominated by foreign capital and ranks among the most open economies in Europe. After the Lehman crash, many of the pre-crisis claims, such as monetary policy’s ‘benign neglect’ approach to asset price development or a strict division of labour between different policy levers, began to be questioned. Even the New Keynesian models, which implied that ceteris paribus price stability would be a sufficient condition for output to remain close to its natural level, were subject to criticism. Studies generally conclude (regarding the pre-crisis development) that the low-interest-rate environment in the precrisis period led to the formation of financial imbalances while associated fiscal stimuli during credit booms only deepened systemic risk (see Taylor, 2009; Obstfeld and Rogoff, 2009, for policy discussion, or Adrian and Liang, 2016, for empirical analysis). The subsequent financial crisis then led to deflationary pressures and an unprecedented (apart from wartime) increase in public debt levels in developed countries. 3 The increase in sovereign default risk further lowered trust in deposit insurance systems and, in general, reduced the ability of fiscal policy to support economic growth. Therefore, it is only logical to question the role of economic policy in determining economic growth and maintaining financial stability. After the crisis, a new set of tools was introduced to reduce the procyclical character of economic policy. Its mandate is to prevent the accumulation of systemic risk and the formation of financial crises using prudentially-tuned measures of macroprudential policy. In the present day, it is becoming obvious that fiscal, monetary, and macroprudential policy affect each other and a strategic conflict may arise in certain situations. Therefore, Borio (2017) claims that counter-cyclical economic policy settings are now crucial, as global debt levels are at a historical high point and room for policy manoeuvring is remarkably narrow. In this context, achieving some form of coordination between economic policy and financial stability objectives seems to be a fundamental task. In fact, keeping the financial sector stable should be in the best interests of both monetary and fiscal policy, given the excessive costs of financial instability. The remainder of the paper is organized as follows: Section 2 serves as a review of the literature published on the topic so far. Section 3 outlines the theoretical underpinnings 3 Bank for International Settlements (2016) states that the public debt-to-GDP ratio increased in developed economies by more than 50 percent. Volume 18, Issue 3, 2018 197 of the empirical framework applied and describes the data employed. Section 4 discusses empirical results, and Section 5 concludes and describes some of the outstanding challenges. Literature review Monetary policy and financial stability A fierce debate on the interaction between economic policy and financial stability erupted after the most recent financial and economic crisis in 2007-2008 and to this point, a number of both theoretical and empirical studies were published. Attention was primarily given to the interplay of monetary policy and asset prices – a relationship that turned out to be crucial prior to the crisis. Kuttner (2013) provides an overview of the empirical findings both prior to and after the crisis. Current generally-accepted theory holds that one may expect asset prices to decrease following a contractionary monetary policy shock through the functioning of an asset-price channel. However, Galí and Gambetti (2015), using a time-varying framework, find protracted periods during which stock prices increase after a monetary tightening. On the other hand, Paul (2018) finds that stock prices always decrease following contractionary monetary policy shocks, while Aastveit et al. (2017) find that Fed episodically took real stock price growth into account. Still, stock price developments do not have a significant impact on monetary policy decision-making due to their higher volatility. In this area, housing prices are frequently discussed, as they are considered an early-warning indicator (Gramlich et al., 2010, Babecky et al., 2013; Laina et al., 2015), and tended to increase sharply before the crisis. Therefore, many authors have argued for the inclusion of residential real estate prices in the consumer price index and the monetary policy decision-making process (e.g Goodhart 2001; Aydin and Volkan, 2011; Hampl and Havranek, 2017). They argue that this could lead to smoother business cycle fluctuations compared to conventional inflation targeting. In this context, there has been a renewed “lean or clean” debate aimed at verifying whether the central bank should incorporate asset price development into its decisionmaking process, even when the current inflationary target is not at risk. Up until the crisis, monetary policy practitioners only responded to asset prices if and when associated risks actually materialized and were transmitted into the real economy (“clean up afterwards” strategy). Even today, this approach has its advocates. Svensson (2016) provides a comprehensive discussion and review of existing empirical studies and concludes that “leaning against the wind” is still not fully justified. However, in light of recent empirical evidence showing that loose monetary policy influences asset prices, risk appetite, and financial stability in general, a growing number of studies favour the leaning against the wind strategy (see Smets, 2014, for a review). Those studies claim that monetary policy should respond to financial risk accumulation to forestall these risks’ materialization and the associated negative impact on the real economy (Filardo and Rungcharoenkitkul, 2016). Following this intense discussion, a consensus of sorts emerged in the form of a macroprudential policy toolset to complement existing capital and liquidity regulations. In many countries, macroprudential policy represents an autonomous branch of economic policy with its own objectives (financial stability) and tools. This new paradigm has Review of Economic Perspectives 198 given rise to a new set of questions regarding the extent to which monetary policy is able to influence financial sector development, as well as the relative positions of monetary and macroprudential policy, and their respective targets. In theory, the impact of monetary policy on financial stability is related to monetary policy transmission channels: the asset-price channel, the bank-lending channel, and the balance-sheet channel. Theory suggests that lower interest rates should strengthen financial stability. Loose monetary policy is, under general circumstances, transmitted to lower lending rates (the bank-lending channel). Thus, loans become cheaper and more attractive, which then increases the volume of assets, the share of loans on total assets, and banks’ profitability. This also improves the balance sheets of economic subjects (the balance-sheet channel). Hoffman and Peersman (2017) also speak of a new channel of monetary policy that works through the debt service ratio, defined as total debt payments to the income of the private non-financial sector. Hoffman and Peersman (2017) and Juselius et al. (2017) argue that monetary expansion leads to a decrease in the debt service ratio, with lower interest rates on the stock of debt outweighing a rise in the debt-to-income ratio. Drehmann and Juselius (2012) and Lombardi et al (2017) further state that changes in the debt service ratio can have aggregate macroeconomic effects and significantly influence financial stability. Also, one must not forget that keeping interest rates low in the long term may induce households and firms to gradually increase leverage through the conventional intertemporal substitution effect (for more detailed discussion see ESRB, 2016, and IMF, 2015). Even commercial banks are influenced by the low interest rate environment, as to maintain their profitability, they need to change their portfolio structure in favour of riskier assets. Borio and Zhu (2012) describe this as the risk-taking channel of monetary policy. Recent empirical studies have already shed some light on its functioning (Angeloni et al., 2014; Abbate and Thaler, 2015; Gilbert et al., 2018, to name a few). Borio (2014a) claims that accumulation of financial disequilibrium occurs most often during positive supply shocks, which push down prices while enhancing optimistic expectations and investment into riskier assets. Several studies stress the need for some form of policy coordination between monetary and macroprudential policy. The need for such coordination stems from the observation that monetary and macroprudential policy tools are not independent, as they affect both the monetary and credit conditions via their effect on credit growth (Malovana and Frait, 2017). At the same time, the best economic outcomes can be expected if both policies are used in a complementary manner and are executed by a single institution (Libich, 2017). Galati and Moessner (2013) provide an overview of research on monetarymacroprudential policy interactions. Fiscal policy and financial stability During the recent financial crisis, extraordinary measures were taken, not only by central banks but by governments as well, to prevent a collapse of the financial sector. Support packages from governments reached unprecedented levels and, despite the fact they might endanger fiscal deficits (Agnello and Sousa, 2009) and long-term debt sustainability (Hallet and Lewis, 2008; Schuknecht et al., 2009), they are being justified by the idea that fiscal policy might be used to help economic recovery and, if executed properly, to foster financial stability. In this spirit, BIS (2016) claims that fiscal policy Volume 18, Issue 3, 2018 199 should be, compared to the current state of affairs, much more countercyclical. Fiscal policy should therefore in times of financial boom generate a budgetary surplus and create sufficient fiscal space for subsequent financial cycle contraction. 4 Governments however often use economic growth to increase mandatory expenditures or government investments, which, despite overall positive economic development, lead to a negative primary balance. Therefore, fiscal policy’s manoeuvring space may be substantially limited, especially due to ill-considered fiscal policy strategies during an economic boom. Still, to this day there is no unified view on the linkages between fiscal policy and asset prices. This is due to the fact that not all asset prices react alike to fiscal policy shocks (Agnello and Sousa, 2013), and that the fiscal and financial sector are inherently interlinked, making it hard to identify specific effects and their direction. To explain the interlinking in a more rigorous manner, let us consider the channels through which changes in sovereign risk may affect banks. One such channel works through the banks’ direct holding of sovereign debt (the asset holding channel). In general, a financial boom often supports sovereign credit, which lowers the risk of sovereign default and improves banks’ portfolios. Also, banks often use sovereign securities as collateral to secure wholesale funding from central banks, private repo markets, and issuance of covered bonds, and to back OTC derivative positions. So, when the price of sovereign bonds increases, the value of the collateral automatically increases as well (the collateral channel). Ari (2016) shows that during a crisis, banks become heavily exposed to domestic sovereign bonds, which may lead to a rise in bank funding costs and the crowding out of bank lending to the private sector. Deev and Hodula (2016) add that in cases where government-owned banks directly participate in large governmental projects, banking fragility may result in the deterioration of state funds while also raising the risk of sovereign default. Also, one must not forget that sovereign ratings often represent a ceiling for the rating of domestic financial institutions, and thus any upgrade in a country’s rating also affects local banks (the ratings channel). These channels, when working in the positive direction during a financial boom, may greatly improve banks’ balance sheets and the availability of additional capital funding. During a financial cycle contraction, however, government debt usually grows because of a decrease in economic activity and asset prices. According to Borio et al. (2015), economic output is negatively influenced for many years after a crisis. 5 During a financial crisis, asset prices significantly decrease and affect consumption and thereby indirect taxes via wealth effects. The growth of non-performing loans and decline in asset prices can also undermine the health of private financial institutions. In such cases, the government may be forced to recapitalize such institutions from public funds (bail-out costs). 6 There are also likely to be second-round effects on fiscal variables, particularly 4 See e.g. BIS papers: Borio and Lowe (2002), Drehmann et al. (2012), Borio (2014b). 5 They state that during boom periods, factors of production are being moved to less productive and more procyclical sectors (e.g. construction). This inevitably deepens and prolongs the economic slowdown during financial stress and also weakens the sustainability of public debt. 6 For the rare occasions when problems in the financial sector reach this scope, a financial crisis resolution mechanism was formed (according to the Bank Recovery and Resolution Directive – BRRD). Their aim is to prevent the need for bank recapitalization from public funds and therefore to prevent a financial crisis having a one-time negative impact on fiscal sustainability. Review of Economic Perspectives 200 when significant financial instability feeds back into the economy (for a more detailed description of transmission channels, see Eschenbach and Schuknecht, 2004). Fiscal-monetary-financial stability interactions Overall, the existing research on fiscaland monetary-financial stability interactions may be grouped into two strands. One strand analyzes the effects of monetary and fiscal policies on the real economy and the financial sector separately, and abstracts from the interlinkages between individual economic policy measures. A review of such studies, although by no means exhaustive, was presented in the previous sub-sections. The second, ever-expanding strand of literature analyzes the behaviour of economic agents in terms of interaction and exchange of information. Over time, it became crucial to account for the fact that both monetary and fiscal policy influence one another and interact with their broad set of instruments. This interaction and the (in)compatibility of their measures give rise to various impacts on the real economy and financial sector. It is reasonable to expect that none of the institutions involved will completely ignore the behaviour of other economic policy agents. The theoretical foundations of fiscal and monetary policy interactions are well established in the literature (Sargent and Wallance, 1981; Leeper, 1991; Woodford, 1996, among others). Empirical attempts to analyze policy interactions may be found e.g. in Muscatelli et al.(2004), Mountford and Uhlig (2009), Rossi and Zubairy (2011) and recently in Bianchi and Ilut (2017) and Orphanides (2017). These studies adopt different approaches to the analysis, but the authors generally agree that monetary and fiscal policies do not contradict themselves in the event of shocks to output (supply or demand shocks) and can act as substitutes in the case of inflation shocks or shocks affecting individual economic policy instruments. We contribute to this literature by providing comprehensive time series evidence on fiscalmonetary-financial stability interactions. We do so in a flexible framework using information from hundreds of macroeconomic time series, which significantly lowers the information bias. We rely mainly on Czech macroeconomic data, but our modelling framework can be extended to other economies. The theoretical foundations of policy and financial stability interactions can be found in Woodford (2011) and Carlstrom et al. (2010). Ueda and Valencia (2014) build a model based on a loss function with three elements: variations of output, inflation, and private sector leverage. They derive the following, rather intuitive equation:       1 ln ee t t t t yy             : , (1) which shows that ex-post leverage in the economy is given by surprises in inflation   e t   , output   e t yy , and credit growth t  . Credit growth is in turn determined by regulatory measures and credit shocks. The relationship described in (1) suggests that the increase of deviation from equilibrium of inflation   e t   and output   e t yy lowers private sector leverage  . Assuming that the financial risk is positively correlated with the level of leverage, eq. (1) suggests that loose economic policies lower financial risks in the economy and therefore the risk of financial instability in the future. In this paper, we argue that the functioning of the economy might not be as straightforward as the theoretical models suggest, as the effect could go either way, i.e., may be positive or negative, and is significantly time-dependent. Volume 18, Issue 3, 2018 201 Empirical methodology To study the fiscal-monetary-financial stability interactions, we use the factoraugmented vector autoregression model (FAVAR) introduced in Bernanke et al. (2005). Our primary motivation for choosing the FAVAR model is to avoid the information bias when identifying the set of economic policy innovations. 7 Also, we want to use the advantageous logic of VAR models, which are a theory–free way to “let the data speak” about causality questions. Last, one can find many types of autoregressive patterns in macroeconomic and financial time series, implying that the real economy does not constitute a random entity. A VAR model accounts for these patterns and, for each variable entering the model, computes an equation explaining the variable’s evolution based on its own and other variables’ lagged values. In contrast to a simple VAR model, the FAVAR model includes unobserved lowdimensional factors in the autoregression, reducing the information bias. The FAVAR model thus utilizes the advantages of a data rich environment, while remaining tractable in terms of the number of parameters to be estimated. We specify an 1M vector of macroeconomic time series t Y and a 1K vector of unobserved factors t F . We assume that the joint dynamics of t F , t Y is given by the following equation:   1 1 . tt t tt FF L YY                   , (2) where   L is a lag polynomial and t  is an error term with zero mean and covariance matrix Q. Equation (2) is a standard VAR model that can be interpreted as a reduced form of a linear rational-expectations model with both observed and unobserved variables. The unobserved variables make the model impossible to estimate; therefore, we assume that the additional informational time series t X are linked to the unobservable factors t F and the observable factors t Y by: fy t t t t X F Y e         , (3) where f  and y  are matrices of factor loadings and t e is a serially uncorrelated error term with a zero mean (innovation shock). Equation (3) captures the idea that both vectors t Y and t F are pervasive forces that might drive the common dynamics of t X . This static representation of the dynamic factor model enables us to estimate the factors by principal components. As the static factors incorporate information from a large number of economic variables, the information set of the structural factor model is far greater than that of a standard VAR. Thus, it becomes unlikely that the information set 7 A situation in which the econometrician's information set is smaller than that of the economic agents. If this is the case, the relatively small number of variables in a small model may not be sufficient to properly identify shocks, which increases the risk of a biased estimate. For example, when trying to identify a monetary policy shock in a VAR model, the shock may actually not be truly exogenous, as it may also capture instances when the central bank endogenously reacts to changing inflation expectations. Forni and Gambetti (2010) demonstrate that non-fundamentals can account for the well-known VAR price puzzle and the delayed overshooting puzzle. Similarly, Iwata (2013) discusses two fiscal policy puzzles and their possible explanations. Review of Economic Perspectives 202 of economic agents will be superior to the information set employed by us. In our application, the we assume that the vector of observable variables t Y holds only our specified innovations, which are then assumed to have pervasive effect on the entire economy. Note that any remaining time series from our vast dataset may be linked to the factors outside of the VAR model via factor loadings identified by the principal component analysis. For details on the estimation procedure, please consult the Appendix C. 8 Data and identification scheme Our vector t X for factor extraction consists of a balanced panel of 140 quarterly time series representing the Czech economy and the rest of the world. They are drawn mainly from the Czech National Bank, Czech Statistical Office, and ECB databases. The data spans the period 2001:Q1 – 2016:Q1. Generally, it is not required to perform any ex ante categorization of data, but we can benefit from stacking data into sub-groups in accordance with the different classes of economic variables for the sake of the clarity of our computational process. The data sub-groups and corresponding variable counts are presented in Table 1 below. Note that prior to estimation, the data was transformed to assure stationarity of the time series using natural logarithms and first differences. By modelling the fiscal-monetary-financial stability interactions in a data rich environment, we control for real economy development, changes in fiscal and monetary policy, financial sector development, and external influences. We identify policy innovations using recursive ordering, placing unobserved factors before observed factors. The principal assumption is that unobserved factors do not respond to policy innovations within the first quarter. In order to identify policy innovations, we divide our panel of variables into two groups: slowand fast-moving variables. Blocks describing the external environment, real economy, fiscal variables, and prices are classed as slow-moving (in the order given in Table 1). A slow-moving variable is one that is largely predetermined in the current period and is assumed not to respond instantaneously to the specified shocks. The rest of the blocks are classed as fastmoving variables, which are assumed to be highly sensitive to contemporaneous economic news or shocks. In case of a fiscal shock, all fiscal variables are classed as fastmoving as in Lagana and Sgro (2011). Note that the variables from which we extract the innovations are always ordered last in the covariance matrix (and treated as a factor on their own). This means that we assume each of the given innovations to affect our latent factors with a lag of one quarter. Since we want to avoid a shortage of degrees of freedom, we prefer to use a smaller model with lower number of lags. The standard information criteria tests suggest three lags, but using more sensitive exclusion-based General-to-Specific approach, we find that two lags are sufficient. Therefore, our baseline FAVAR model contains two lags. Nevertheless, we check for the robustness of our results later. 8 The FAVAR modelling framework is used in many economic applications, see for instance Forni and Gambetti (2010), Eickmeier and Hofmann (2013), Aastveit (2013) or Hodula and Pfeifer (2018). Volume 18, Issue 3, 2018 203 Table 1 Sub-groups in the dataset Data Sub-Groups Slow/Fast Moving Number of Variables External environment (S) 12 Real economy (S) 32 Labour market (S) 17 Government (S) 12 Prices and price expectations (S) 20 Interest rates and credit (F) 29 Financial sector (F) 12 Exchange rates (F) 6 Note: The Appendix lists the time series included in these sub-groups. Sub-groups highlighted in bold contain variables used as the source of an identified shock. Such variables are never included in the dataset from which we extract the factors. We consider three types of economic policy shocks: monetary and fiscal policy expansion and systemic risk materialization. The main policy tool of the CNB is a two-week repo rate. However, because the repo rate does not change continuously but only as an outcome of CNB Board of Governors meetings, we use the inter-bank rate to proxy for the CNB’s key monetary policy rate; similar to how it is done in the CNB’s own forecasting system. Hence, we identify an expansionary monetary policy shock as a decrease in PRIBOR 3M. From the financial stability standpoint, an interest rate drop increases the economy’s leverage and boosts the risk appetite of economic subjects, and therefore may threaten financial stability. The main fiscal policy variables are government revenue from taxes and total government expenditure, either of which may be used as a source of the innovations. However, using government revenue from taxes as a source variable increases the risk of endogeneity, since government revenue growth is associated with business cycle expansion. On the basis of these shortcomings, we identify an expansionary fiscal policy shock as an increase in total government expenditure. 9 Last but not least, we consider the impact of systemic risk materialization as part of financial cycle development. We draw this information from the non-performing loans (NPL) ratio time series. During financial stress, the NPL ratio grows because of an increase in the absolute level of non-performing loans and a decrease in newly-granted loans. Hence, the systemic risk materialization shock is identified as an increase in the NPL ratio. We conduct a number of checks to verify the robustness of our results. First, we use alternative variables and time frames during the identification of shocks. In case of monetary policy expansion, we check the robustness of results with respect to the CNB’s exchange rate commitment, which started in November 2013, and the model with data ending in 2012Q1. 10 By cutting the sample, we also address the fact that the 9 Additionally, while changes in asset prices may influence government revenues, they are much less likely to affect government spending. 10 For more details on the exchange rate commitment (and recent exit) please consult the CNB website at https://www.cnb.cz/en/monetary_policy/exit_exchange_rate_commit/index.html Review of Economic Perspectives 210 Figure 4 Systemic risk materialization, policy responses and the financial sector Notes: Median impulse responses are reported with 90% probability bands. The y-axis measures the strength of the variable’s response in percentage points; the x-axis is in quarters after the shock. All variables enter the model as annualized percentage growth. Figure 4 gives indications as to the causes behind the systemic risk materialization in the financial sector: With the decreased ability of economic agents to repay their loans, we document a rather sharp decline in both credit dynamics and housing prices. This adverse market situation affects mainly commercial banks, which collectively become much more risk-averse, changing their portfolios in favor of less risky assets (in practice, this would manifest as increased exposure to the government and central bank). This is a simple signaling strategy wherein banks strive to present themselves as stable to decrease the cost of additional capital, which, due to the crisis, generally increases. In spite of decreasing risk-weighted assets (outcome of the above-mentioned portfolio cleansing), capital surplus significantly decreases. This might also be achieved by simply increasing the capital requirements. Materialization of systemic risk increases loss provisions and risk costs, which represent a significant component of the interest rate on loans. Growth of interest rates on loans to the private sector increases the debt service ratio and might lead to further deterioration of the loan portfolio. Conclusions We explore situations in which fiscal, monetary, and financial stability may interact, and contribute to the discussion about the related policies’ coordination and potential -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0 5 10 15 Housing price index -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0 5 10 15 Loans to private sector -0,4 -0,2 0,0 0,2 0 5 10 15 Risk-weighted assets -0,4 -0,2 0,0 0,2 0,4 0,6 0 5 10 15 Debt service ratio -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 1,2 0 5 10 15 Loss provisions -0,3 -0,2 -0,1 0,0 0,1 0,2 0,3 0 5 10 15 Capital surplus NPL ratio increase Volume 18, Issue 3, 2018 211 conflicts. We highlight the need to take into consideration both fiscal and monetary policy, as they may both affect asset prices and credit dynamics and contribute to systemic risk accumulation and therefore influence financial cycle amplitude. For this purpose, we construct a FAVAR model to capture the policy interplay in a data-rich environment. The FAVAR model helps ensure that the estimated impulse responses are invariant with respect to extensions of the information set – an issue that often plagues impulse response results. The presented model enables us to identify several patterns. First, loose economic policies have a positive impact on financial stability in the short term, but this effect may reverse in the medium term. In greater detail, we find that fiscal and monetary policylike shocks impact the economy and financial sector differently. While fiscal expansion influences credit dynamics and the related set of financial sector variables in the short term and then quickly fades out, monetary expansion seems to benefit financial stability at first, but its effects turn negative in the medium term. This somewhat complicates the answers to the first two research questions presented in the introduction: For the first question, loose economic policies might indeed benefit financial stability in the short term, but (if kept accommodative long enough) they will eventually work in the opposite direction, towards instability. For the second, yes, fiscal and monetary policy shocks impact the financial sector differently. We highlight the need to consider the full policy mix (fiscal and monetary policy effects) when formulating appropriate policy measures. Regarding our third research question, we find that financial imbalances (systemic risk materialization) cause losses in output, damage public finances, and trigger deflationary pressures. Furthermore, systemic risk materialization leads to increases in the debt service ratio and might lead to further deterioration of the loan portfolio. Overall, our results confirm the need to discuss and coordinate changes in economic policy to avoid potential conflict situations and surprises in the market. Going beyond existing research, we also provide time-series evidence showing that macroprudential policy cannot view fiscal policy as Ricardian (passive), but must consider it similarly to how it views monetary policy. Further, our finding that both fiscal and monetary policy may be transmitted into the financial sector and influence the risk-taking behavior of economic agents provides support for countercyclical fiscal and monetary policy. Therefore, we make several policy recommendations that may decrease the procyclical character of economic policy and mitigate possible conflict situations at low implementation cost: In the fiscal policy area, we believe implementing a fiscal rule to restrict the procyclical character of public finances and stabilize government debt over the long term would be beneficial. However, as the Bank for International Settlements (2016) states, it is important to augment the fiscal rule with a proper automatic stabilization scheme, and apply it ex ante when certain economic conditions are met. Further, Borio et al. (2013) recommend including information about financial cycle development into the potential product and output gap estimation. Another possibility for mitigating the unwanted fiscal policy procyclicality associated with systemic risk build-up would be to partially adjust the taxation system in order to limit the growth of certain types of credit. Given the procyclical development of residential property prices and loans to the private sector, Review of Economic Perspectives 212 it would be beneficial to limit mortgage tax relief (see Andrews et al., 2011). 13 The European Commission (2015) has encouraged Member States to ensure that tax systems do not encourage household debt. Lombardi et al. (2017) give evidence of the negative impact of high household indebtedness on GDP growth over the long term. There are possibilities to limit the impact of sovereign risk on the banking sector. The Czech National Bank was among the first to implement methods for public finance stress testing with special capital requirements on banks with higher sovereign default risk on government bonds (CNB, 2016). The impact of monetary policy actions on financial stability may be partially mitigated by incorporating residential property prices in the targeted consumer price index or by using a broader index (Hampl and Havranek, 2017, describe CPIH on the case of the Czech Republic) as a supplementary indicator for monetary policy decisions. There are also other, more controversial propositions for mitigating the procyclical character of monetary policy. Borio (2016) suggests the use of an augmented Taylor rule, in which the monetary authority should respond not only to the inflation gap but also to some defined financial stability gap, and state that a monetary policy rule that takes financial developments into account could help reduce the financial cycle, leading to higher output in the long run. Acknowledgements: We would like to thank Jaromir Baxa, Ales Melecky and two anonymous referees for their helpful comments on earlier versions of this paper. We would also like to thank the seminar participants at the 2016 Conference on Currency, Banking, and International Finance, the 2016 European Financial Systems Conference, Technical University of Ostrava, and the Slovak Academy of Sciences for their comments. The views and opinions expressed herein are those of the authors and do not represent the views of their institutions. All errors and omissions remain entirely the fault of the authors. Funding: This work was supported by the 2017 research grant (SP2017/110) from Technical University of Ostrava and the Czech Science Agency, grant Anti-cyclical policies and external equilibrium in a model of inflation targeting, No. 18-12340S. Disclosure statement: No potential conflict of interest was reported by the authors. References AASTVEIT, K. A. (2013). Oil price shocks and monetary policy in a data-rich environment. Working paper series 10/2013, Norges Bank (Central Bank of Norway). AASTVEIT, K., FURLANETTO, F., & LORIA, F. (2017). Has the fed responded to house and stock prices? A time-varying analysis. Working paper series 01/2017, Norges Bank (Central Bank of Norway). ABBATE A., & THALER, D. (2015). Monetary policy and the asset risk-taking channel. Discussion paper 48/2015, Deutsche Bundesbank. 13 Bank for International Settlements (2016) illustrates the relationship between mortgage tax relief and household debt. Volume 18, Issue 3, 2018 213 ADRIAN, T., & LIANG, N. (2016). Monetary policy, financial conditions, and financial stability. Staff report 690, Federal Reserve Bank of New York. AGNELLO, L., & SOUSA, R. M. (2009). The determinants of public deficit volatility. Working paper 1042, European Central Bank. AGNELLO, L., & SOUSA, R. M. (2013). Fiscal policy and asset prices. Bulletin of Economic Research, 65(2), 154-177. DOI: 10.1111/j.0307-3378.2011.00420.x AMBRISKO, R., BABECKY, J., RYSANEK, J., & VALENTA, V. (2015). Assessing the impact of fiscal measures on the Czech economy. Economic Modelling, 55(1), 350357. DOI: 10.1016/j.econmod.2014.07.021 ANDREWS, D., CALDERA SÁNCHEZ, A., & JOHANSSON, A. (2011). Housing markets and structural policies in OECD countries. Economics department working papers 836, OECD. ANGELONI, I., FAIA, E., & LO DUCA, M. (2017). Monetary policy and risk taking. Journal of Economic Dynamics & Control, 52, 285-307. DOI: 10.1016/j.jedc.2014.12.001 ARI, A. (2016). Sovereign risk and bank risk-taking. Working paper series 1894/2016, European Central Bank. AYDIN, B., & VOLKAN, E. (2011). Incorporating financial stability in inflation targeting framework. Working paper 11/224, International Monetary Fund. BABECKY, J., HAVRANEK, T., MATEJU, J., RUSNAK, M., SMIDOVA, K., & VASICEK, B. (2013). Leading indicators of crisis incidence: evidence from developed countries. Journal of International Money and Finance, 35, 1-19. DOI: 10.1016/j.jimonfin.2013.01.001 BERNANKE, B., BOIVIN, J., & ELIAZS, P. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics, 120 (1), 387-422. BIANCHI, F., & ILUT, C. (2017). Monetary/fiscal policy mix and agent's beliefs. Review of Economic Dynamics, 26, 113-139. DOI: 10.1016/j.red.2017.02.011 BANK FOR INTERNATIONAL SETTLEMENTS (2016). Towards a financial stability-oriented fiscal policy. Basel: Bank for International Settlements. BLANCHARD, O., & PEROTTI, R. (2002). An empirical characterization of the dynamic effects of changes in government spending and taxes on output. The Quarterly Journal of Economics, 117(4), 1329−1368. DOI: 10.1162/003355302320935043 BORIO, C. (2014a). The financial cycle and macroeconomics: what have we learnt? Journal of Banking & Finance, 45(1), 182–98. DOI: 10.1016/j.jbankfin.2013.07.031 BORIO, C. (2014b). The international monetary and financial system: its Achilles heel and what to do about it. Working Paper 456, Bank for International Settlements. BORIO, C. (2016). Revisiting three intellectual pillars of monetary policy. Cato Journal, 36(2), 213-238. Review of Economic Perspectives 214 BORIO, C. (2017). Secular stagnation or financial cycle drag? Business Economics, 52(2), 87-98. DOI: 10.1057/s11369-017-0035-3 BORIO, C., & Lowe, P. (2002). Asset prices, financial and monetary stability: exploring the nexus. Working paper 114, Bank for International Settlements. BORIO, C., DISYATAT, P., & JUSELIUS, M. (2013). Rethinking potential output: embedding information about the financial cycle. Working Paper 404, Bank for International Settlements. BORIO, C., KHARROUBI, E., UPPER, Ch., & ZAMPOLLI, F. (2015). Labour reallocation and productivity dynamics: financial causes, real consequences. Working Paper 534, Bank for International Settlements. BORIO, C., & ZHU, H. (2012). Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism? Journal of Financial Stability, 8(4), 236251. DOI. 10.1016/j.jfs.2011.12.003 BORYS, M., FRANTA, M., & HORVATH, R. (2009). The effects of monetary policy in the Czech Republic: an empirical study. Empirica, 36(1), 419-443. DOI: 10.1007/s10663-009-9102-y CARLSTROM, Ch, FUERST, T., & PAUSTIAN, M. (2010). Optimal monetary policy in a model with agency costs. Journal of Money, Credit and Banking, 42(6), 37-70. DOI: 10.1111/j.1538-4616.2010.00329.x DEEV, O., & HODULA, M. (2016). Sovereign default risk and state-owned bank fragility in emerging markets: evidence from China and Russia. Post-Communist Economies, 28(2), 232-248. DOI: 10.1080/14631377.2016.1164438 DREHMANN, M., & JUSELIUS, M. (2012). Do debt service costs affect macroeconomic and financial stability? BIS Quaterly Review, September 2012, 21-35. DREHMANN, M., BORIO, C., & TSATSARONIS, K. (2012). Characterizing the financial cycle: don't lose sight of the medium term! Working Paper 380, Bank for International Settlements. DUNGEY, M., & FRY, R. (2009). The identification of fiscal and monetary policy in a structural VAR. Economic Modelling, 26(6), 1147-1160. DOI: 10.1016/j.econmod.2009.05.001 EDGE, R. M., & MEISENZAHL, R. R. (2011). The unreliability of Credit-to-GDP ratio gaps in real time: implications for countercyclical capital buffers. International Journal of Central Banking, 7(4), 261-298. EICKMEIER, S. & HOFMANN, B. (2013). Monetary policy, housing booms and financial (im)balances. Macroeconomic Dynamics, 17(4), 830-860. DOI: 10.1017/S1365100511000721 ESCHENBACH, F., & SCHUNKNECHT, L. (2004). The fiscal costs of financial instability revisited. Economic Policy, 14(1), 313-346. ESRB (2016). Macroprudential policy beyond banking: an ESRB strategy paper. Frankfurt am Main: European System Risk Board. Volume 18, Issue 3, 2018 215 FILARDO, A., & RUNGCHAROENKITKUL, P. (2016). A quantitative case for leaning against the wind. Working Paper 594, Bank for International Settlements. FORNI, M., & GAMBETTI, L. (2010). The dynamic effects of monetary policy: A structural factor model approach. Journal of Monetary Economics, 57(1), 203−216. DOI: 10.1016/j.jmoneco.2009.11.009 FRANTA, M. (2012). Macroeconomic effects of fiscal policy in the Czech Republic: evidence based on various identification approaches in a VAR framework. Working Paper 13/2012, Czech National Bank. GALATI, G. & MOESSNER, R. (2013) Macroprudential policy – a literature review. Journal of Economic Surveys 27(5), 846-878. GALÍ, J., & GAMBETTI, L. (2015) The effects of monetary policy on stock market bubbles: some evidence. American Economic Journal: Macroeconomics, 7(1), 233–57. GERSL, A., & SEIDLER, J. (2015). Countercyclical capital buffers and Credit-to-GDP gaps: simulation for central, eastern, and southeastern Europe. Eastern European Economics, 53(6), 439-465. DOI: 10.1080/00128775.2015.1102602 GILBERT C., LEVIEUGEA, G., & POPESCU, A. (2018). Monetary policy and longrun systemic risk-taking. Journal of Economic Dynamics and Control, 86, 165-184. DOI: 10.1016/j.jedc.2017.11.001 GOODHART, C. (2001). What Weight Should Be Given to Asset Prices in the Measurement of Inflation? The Economic Journal, 111(472), 335-356. DOI: 10.1111/14680297.00634 GRAMLICH, D., Miller, G. L., OET, M. V., & ONG, S. J. (2010). Early warning systems for systemic banking risk: critical review and modeling implications. Banks and Bank Systems, 5(2), 199-211. HALLETT, A. H., & LEWIS, J. (2008). European fiscal discipline before and after EMU: crash diet or permanent weight loss? Macroeconomic Dynamics, 12(3), 404–24. DOI: 10.1017/S1365100507070204 HAMILTON, J. D. (2017, in press). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics. DOI: 10.1162/REST_a_00706 HAMPL, M., & HAVRANEK, T. (2017). Should inflation measures used by central banks incorporate house prices? the Czech National Bank’s approach. Research and Policy Note 1/2017, Czech National Bank. HAUG, A., JEDRZEJOWIC, T., & SZNAJDERSKA, A. (2013). Combining monetary and fiscal policy in an SVAR for a small open economy. Working Paper 168, Narodowy Bank Polski (Central bank of Poland). HODULA, M., & PFEIFER, L. (2018). The impact of credit booms and economic policy on labour productivity: a sectoral analysis. Acta VŠFS, 12(1), 10‒42. HOFFMAN, B., & PEERSMAN, G. (2017). Is there a debt service channel of monetary transmission? BIS Quarterly Review, December 2017, 23-37. Review of Economic Perspectives 216 IMF (2015). Monetary policy and financial stability. Washington: International Monetary Fund. IWATA, Y. (2013). Two fiscal policy puzzles revisited: new evidence and an explanation. Journal of International Money and Finance, 33(C), 188−207. DOI: 10.1016/j.jimonfin.2012.11.015 JUSELIUS, M., BORIO, C., DISYATAT, P., & DREHMANN, M. (2017). Monetary policy, the financial cycle, and ultra-low interest rates. International Journal of Central Banking, 13(3), 55–90. KILIAN, L. (1988). Small-sample confidence intervals for impulse response functions. Review of Economics and Statistics, 80 (2), 218-230. DOI: 10.1162/003465398557465 KUTTNER, K. N. (2013). Low interest rates and housing bubbles: still no smoking gun. In Douglas D. Evanoff et al. (ed.), The Role of Central Banks in Financial Stability, pp. 218-230. Singapore: World Scientific Publishing. LAEVEN, L., & VALENCIA, F. (2010). Resolution of banking crises: the good, the bad, and the ugly. Working Paper 146, International Monetary Fund. LAEVEN, L., & VALENCIA, F. (2013). Systemic banking crises database. IMF Economic Review, 61(2), 225-270. DOI: 10.1057/imfer.2013.12 LAGANA, G., & SGRO, P. M. (2011). A factor-augmented VAR approach: the effect of a rise in the US personal income tax rate on the US and Canada. Economic Modelling, 28(3), 1163-1169. DOI: 10.1016/j.econmod.2010.12.007 LAINA, P., NYHOLM, J., & SARLIN, P. (2015). Leading Indicators of Systemic Banking Crises: Finland in a panel of EU countries. Working paper 1758, European Central Bank. LEEPER, E. M. (1991). Equilibria under active and passive monetary policies. Journal of Monetary Economics, 27(1), 129-147. DOI: 10.1016/0304-3932(91)90007-B LIBICH, J. (2017). Unpleasant monetarist arithmetic: macroprudential edition. Working Paper 2017-40, Centre for Applied Macroeconomic Analysis. LOMBARDI, M., MOHANTY, M., & SHIM, I. (2017). The real effects of household debt in the short and long run. Working Paper 607, Bank for International Settlements. MALOVANÁ, S., & FRAIT, J. (2017). Monetary policy and macroprudential policy: rivals or teammates? Journal of Financial Stability, 32, 1-16. 10.1016/j.jfs.2017.08.004 MOUNTFORD, A., & UHLIG, H. (2009). What are the effects of fiscal policy shocks? Journal of Applied Econometrics, 24(6), 960-992. 10.1002/jae.1079 MUSCATELLI, V.A., TIRELLI, P., & TRECROCI, C. (2004). Fiscal and monetary policy interactions: empirical evidence and optimal policy using a structural New Keynesian model. Journal of Macroeconomics, 26, 257-280. DOI: 10.1016/j.jmacro.2003.11.014 OBSTFELD, M., & ROGOFF, R. (2009). Global imbalances and the financial crisis: Products of common causes. Econometrics laboratory. Discussion Paper 7606, Centre for Economic Policy Research. Volume 18, Issue 3, 2018 217 ORPHANIDES, A. (2017). The fiscal-monetary policy mix in the euro area: challenges at the zero lower bound. Discussion Paper 060, European Commission. PAUL, P. (2018). The Time-Varying Effect of Monetary Policy on Asset Prices. Working Paper 2017-09, Federal Reserve Bank of San Francisco. PLASIL M., KONECNY, T., SEIDLER, J., & HLAVAC, P. (2015). In the quest of measuring the financial cycle. Working Paper 5-2015, Czech National Bank. REINHART, C. M., & ROGOFF, S. K. (2013). Banking crises: an equal opportunity menace. Journal of Banking & Finance, 37(11), 4557-4573. DOI: 10.1016/j.jbankfin.2013.03.005 ROSSI, B., & ZUBAIRY, S. (2011). What is the importance of monetary and fiscal shocks in explaining U.S. macroeconomic fluctuations? Journal of Money, Credit and Banking, 43(6), 1247-1270. DOI: 10.1111/j.1538-4616.2011.00424.x SARGENT, T. J., & WALLACE, N. (1991). Some unpleasant monetarist arithmetic. Federal Reserve Bank of Minneapolis Quarterly Review, 5(3), 1-17. SCHUKNECHT, L., von HAGEN, J., & WOLSWIJK G. (2009). Government risk premiums in the bond market: EMU and Canada. European Journal of Political Economy, 25, 371–84. DOI: 10.1016/j.ejpoleco.2009.02.004 SMETS, F. (2014). Financial stability and monetary policy: how closely interlinked? International Journal of Central Banking, 10(2), 263-300. STOCK, J. H., & WATSON, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179. DOI: 10.1198/016214502388618960 SVENSSON, L. E. (2016). Cost-benefit analysis of leaning against the wind: Are costs larger with less effective macroprudential policy? Working Paper 16/3, International Monetary Fund. TAYLOR, J. B. (2009). Economic policy and the financial crisis: an empirical analysis of what went wrong. Critical Review. A Journal of Politics and Society, 21, 341-364. TUZCUOGLU, K., & HOKE, S. H. (2016). Interpreting the latent dynamic factors by threshold FAVAR model. Working Paper 622, Bank of England. UEDA, K., & VALENCIA, F. (2014). Central bank independence and macro-prudential regulation. Economics Letters, 125(2), 327-330. DOI: 10.1016/j.econlet.2013.12.038 WOODFORD, M. (1996). Control of the public debt: A requirement for price stability? Working Paper 5684, National Bureau of Economic Research. WOODFORD, M. (2011). Simple analytics of the government expenditure multiplier. American Economic Journal: Macroeconomics, 3(1), 1-35. Review of Economic Perspectives 218 Appendix A. Data description Table 1. Data description and sources Group No. Series description Unit Source TC S/F Real economy 1 Industrial production index, industry total 2010=100 CSO -Industry, energy 2* S 2 Industrial production index, mining and quarrying 2010=100 CSO -Industry, energy 2* S 3 Industrial production index, manufacturing 2010=100 CSO -Industry, energy 2* S 4 Industrial production index, electricity, gas, steam and air conditioning 2010=100 CSO -Industry, energy 2* S 5 Sales from industrial activity, industry total 2010=100 CSO -Industry, energy 2* S 6 Sales from industrial activity, mining and quarrying 2010=100 CSO -Industry, energy 2* S 7 Sales from industrial activity, manufacturing 2010=100 CSO -Industry, energy 2* S 8 Sales from industrial activity, electricity, gas, steam and air conditioning 2010=100 CSO -Industry, energy 2* S 9 Direct export sales, industry total 2010=100 CSO -Industry, energy 2* S 10 Direct export sales, mining and quarrying 2010=100 CSO -Industry, energy 2* S 11 Direct export sales, manufacturing 2010=100 CSO -Industry, energy 2* S 12 Domestic sales, industry total 2010=100 CSO -Industry, energy 2* S 13 Domestic sales, mining and quarrying 2010=100 CSO -Industry, energy 2* S 14 Domestic sales, manufacturing 2010=100 CSO -Industry, energy 2* S 15 Domestic sales, electricity, gas, steam and air conditioning supply 2010=100 CSO -Industry, energy 2* S 16 New industrial orders, industry total 2010=100 CSO -Industry, energy 2* S 17 Non-domestic new orders 2010=100 CSO -Industry, energy 2* S 18 Domestic new orders 2010=100 CSO -Industry, energy 2* S 19 Construction production index 2010=100 CSO - Construction 2* S 20 House price index, buildings 2010=100 CSO - Construction 2* S 21 House price index, civil engineering works 2010=100 CSO - Construction 2* S 22 Retail trade receipts 2010=100 CNB, ARAD 2* S 23 Gross domestic product, market prices Millions CZK CSO - GDP 2* S 24 GDP deflator 2010=100 CNB, ARAD 2* S 25 Final consumption expenditures, total, current prices Millions CZK CSO - GDP 2* S 26 Final consumption expenditures, households, current prices Millions CZK CSO - GDP 2* S 27 Final consumption expenditures, government, current prices Millions CZK CSO - GDP 2* S 28 Final consumption expenditures, non-profit organisations, current prices Millions CZK CSO - GDP 2* S 29 Gross capital formation, total, current prices Millions CZK CSO - GDP 2* S 30 Export, current prices Millions CZK CSO - GDP 2* S 31 Import, current prices Millions CZK CSO - GDP 2* S 32 Real gross domestic income Millions CZK CSO - GDP 2* S 33 Debt service ratios for the private non-financial sector % BIS database 2 S Labour market 34 Industry total, average number of persons employed (ANPE) no. of persons CSO -Industry, energy 2* S 35 Industry, mining and quarrying, ANPE no. of persons CSO -Industry, energy 2* S 36 Industry, manufacturing, ANPE no. of persons CSO -Industry, energy 2* S 37 Industry, electricity, gas, steam and air conditioning supply, ANPE no. of persons CSO -Industry, energy 2* S 38 Industry total, average gross nominal wage (AGNW) CZK per person CSO -Industry, energy 2* S 39 Industry, mining and quarrying, AGNW CZK per person CSO -Industry, energy 2* S 40 Industry, manufacturing, AGNW CZK per person CSO -Industry, energy 2* S 41 Industry, electricity, gas, steam and air conditioning supply, AAGNWNPE CZK per person CSO -Industry, energy 2* S 42 Construction total, average number of persons employed (ANPE) no. of persons CSO - Construction 2* S 43 Construction total, average gross nominal wage (AGNW) CZK per person CSO - Construction 2* S 44 Employees total, hours worked thousand hours CSO - GDP 2* S 45 Employees, Agriculture, forestry and fishing thousand hours CSO - GDP 2* S 46 Employees, Manufacturing, mining and quarrying and other industry thousand hours CSO - GDP 2* S 47 Employees, Construction thousand hours CSO - GDP 2* S 48 Employees, Trade, transportation, accommodation and food service thousand hours CSO - GDP 2* S 49 Employees, Public administration, education, health and social work thousand hours CSO - GDP 2* S 50 General unemployment rate of the aged 15 to 64 years % CNB, ARAD 1* S Volume 18, Issue 3, 2018 219 51 Job Vacancies thousand CNB, ARAD 2* S 52 Unplaced job seekers thousand CNB, ARAD 2* S Government 53 Government debt, total Millions CZK CSO - Government 2* S 54 Debt securities, total Millions CZK CSO - Government 2* S 55 Debt securities, short-term Millions CZK CSO - Government 2* S 56 Debt securities, long-term Millions CZK CSO - Government 2* S 57 Government loans, total Millions CZK CSO - Government 2* S 58 Government loans, short-term Millions CZK CSO - Government 2* S 59 Government loans, long-term Millions CZK CSO - Government 2* S 60 Debt interests payed Millions CZK CSO - Government 2* S 61 Government expenditures, total Millions CZK CSO - Government 2* S 62 Government revenue, total Millions CZK CSO - Government 2* S Prices and price expectations 63 Consumer Price Index (CPI), total 2015 = 100 CNB, ARAD 2* S 64 CPI, food and non-alcoholic beverages 2015 = 100 CSO - Prices 2* S 65 CPI, alcoholic beverages, tobacco 2015 = 100 CSO - Prices 2* S 66 CPI, clothing and footwear 2015 = 100 CSO - Prices 2* S 67 CPI, housing, water, electricity, gas and other fuels 2015 = 100 CSO - Prices 2* S 68 CPI, furnishings, household equipment, routine maintenance 2015 = 100 CSO - Prices 2* S 69 CPI, health 2015 = 100 CSO - Prices 2* S 70 CPI, transport 2015 = 100 CSO - Prices 2* S 71 CPI, communications 2015 = 100 CSO - Prices 2* S 72 CPI, recreation and culture 2015 = 100 CSO - Prices 2* S 73 CPI, education 2015 = 100 CSO - Prices 2* S 74 CPI, restaurants and hotels 2015 = 100 CSO - Prices 2* S 75 CPI, miscellaneous goods and services 2015 = 100 CSO - Prices 2* S 76 Industrial Producer Prices (IPP), total 2015 = 100 CSO - Prices 2* S 77 IPP, mining and quarrying 2015 = 100 CSO - Prices 2* S 78 IPP, manufacturing 2015 = 100 CSO - Prices 2* S 79 IPP, electricity, gas, steam and air conditioning supply 2015 = 100 CSO - Prices 2* S 80 IPP, water supply; sewerage, waste management and remediation 2015 = 100 CSO - Prices 2* S 81 Market services price indices in the business sphere, total 2015 = 100 CSO - Prices 2* S 82 Inflation expectations of non-financial corporations and companies % CNB, ARAD 1 F 83 Financial market inflation expectations % CNB, ARAD 1 F Interest rates and credits 84 Repo rate - 2 weeks % CNB, ARAD 1 F 85 PRIBOR 3M % CNB, ARAD 1 F 86 PRIBOR 1Y % CNB, ARAD 1 F 87 Government bond yield 2Y % CNB, ARAD 1 F 88 Government bond yield 5Y % CNB, ARAD 1 F 89 Government bond yield 10Y % CNB, ARAD 1 F 90 Bank interest rates on CZK-denominated loans, households total % CNB, ARAD 1 F 91 Bank interest rates on CZK-denominated loans, households, up to 1Y % CNB, ARAD 1 F 92 Bank interest rates on CZK-denominated loans, households, up to 5Y % CNB, ARAD 1 F 93 Bank interest rates on CZK-denominated loans, households, over 5Y % CNB, ARAD 1 F 94 Bank interest rates on CZK-denominated loans, households consumer credit - total % CNB, ARAD 1 F 95 Bank interest rates on CZK-denominated loans, households for house purchase - total % CNB, ARAD 1 F 96 Bank interest rates on CZK-denominated loans, households other loans - total % CNB, ARAD 1 F 97 Bank interest rates on CZK-denominated loans, non-financial corporations % CNB, ARAD 1 F 98 Bank interest rates on CZK-denominated loans, non-financial corporations, up to 1Y % CNB, ARAD 1 F 99 Bank interest rates on CZK-denominated loans, non-financial corporations, up to 5Y % CNB, ARAD 1 F 100 Bank interest rates on CZK-denominated loans, non-financial corporations, over 5Y % CNB, ARAD 1 F 101 Monetary base, monthly average Billions CZK CNB, ARAD 2 F 102 Monetary aggregate M1 Millions CZK CNB, ARAD 2 F 103 Monetary aggregate M2 Millions CZK CNB, ARAD 2 F 104 Loans to residents and non-residents - MFIs Millions CZK CNB, ARAD 2 F