The department of macroeconomics analyses economic fluctuations of important economic indicators as GDP, employment, and interest rates in the short and medium horizon, the impact of economic policy on these, and the institutional framework that determines long term growth and the business cycle. Founded on this research, the department offers policy advice.
Employing 20 experts, the department is able to cover a wide range of macroeconomic issues. The research is focused on development, implementation and application of quantitative macroeconomic models and the analysis of the interaction between the financial markets and the real economy.
Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
in: Econometrica, forthcoming
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
Does Machine Learning Help us Predict Banking Crises?
in: Journal of Financial Stability, forthcomingread publication
Nowcasting East German GDP Growth: a MIDAS Approach
in: Empirical Economics, forthcoming
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
in: Empirica, forthcoming
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.
Switching to Good Policy? The Case of Central and Eastern European Inflation Targeters
in: Macroeconomic Dynamics, forthcoming
The paper analyzes how actual monetary policy changed following the official adoption of inflation targeting in the Czech Republic, Hungary, and Poland and how it affected the volatilities of important macroeconomic variables in the years thereafter. To disentangle the effects of the policy shift from exogenous changes in the volatilities of these variables, a Markov-switching dynamic stochastic general equilibrium model is estimated that allows for regime switches in the policy parameters and the volatilities of shocks hitting the economies. Whereas estimation results reveal periods of high and low volatility for all three economies, the presence of different policy regimes is supported by the underlying data for the Czech Republic and Poland, only. In both economies, monetary policy switched from weak and unsystematic to strong and systematic responses to inflation dynamics. Simulation results suggest that the policy shifts of both central banks successfully reduced inflation volatility in the following years. The observed reduction in output volatility, on the other hand, is attributed more to a reduction in the size of external shocks.
The Evolution of Monetary Policy in Latin American Economies: Responsiveness to Inflation under Different Degrees of Credibility
in: IWH Discussion Papers, No. 9, 2020
This paper investigates the forward-lookingness of monetary policy related to stabilising inflation over time under different degrees of central bank credibility in the four largest Latin American economies, which experienced a different transition path to the full-fledged inflation targeting regime. The analysis is based on an interest rate-based hybrid monetary policy rule with time-varying coefficients, which captures possible shifts from a backward-looking to a forward-looking monetary policy rule related to inflation stabilisation. The main results show that monetary policy is fully forward-looking and exclusively reacts to expected inflation under nearly perfect central bank credibility. Under a partially credible central bank, monetary policy is both backward-looking and forward-looking in terms of stabilising inflation. Moreover, monetary authorities put increasingly more priority on stabilising expected inflation relative to actual inflation if central bank credibility tends to improve over time.
How Does Economic Policy Uncertainty Affect Corporate Debt Maturity?
in: IWH Discussion Papers, No. 6, 2020
This paper investigates whether and how economic policy uncertainty affects corporate debt maturity. Using a cross-country firm-level dataset for France, Germany, Spain, and Italy from 1996 to 2010, we find that an increase in economic policy uncertainty is significantly associated with a shortened debt maturity. Specifically, a 1% increase in economic policy uncertainty is associated with a 0.22% decrease in the long-term debt-to-assets ratio and a 0.08% decrease in debt maturity. Moreover, the impacts of economic policy uncertainty are stronger for innovation-intensive firms. We use firms‘ flexibility in changing debt maturity and the deviation to leverage target to gauge the causal relationship, and identify the reduced investment and steepened term structure as transmission mechanisms.
Capital Account Liberalisation Does Worsen Income Inequality
in: IWH Discussion Papers, No. 7, 2020
This study examines the relationship between capital account liberalisation and income inequality. Adopting a novel identification strategy, namely a difference-in-difference estimation combined with propensity score matching between the liberalised and closed countries, we provide robust evidence that opening the capital account is associated with an adverse impact on income inequality in developing countries. The main findings are threefold. First, fully liberalising the capital account is associated with a small rise of 0.07-0.30 standard deviations in the Gini coefficient in the short-run and a rise as large as 0.32-0.62 standard deviations in the ten years after liberalisation, on average. Second, widening income inequality is the outcome of the growing income share of the rich at the cost of the poor. The long-term effect of capital account liberalisation includes a reduction in the income share of the poorest half by 2.66-3.79 percentage points and an increase in the income share of the richest 10% by 5.19-8.76 percentage points. Third, the directions and categories of capital account liberalisation matter. Inward capital account liberalisation is more detrimental to income equality than outward capital account liberalisation, and free access to the international equity market exacerbates income inequality the most, while foreign direct investment has an insignificant impact on inequality.
Integrated Assessment of Epidemic and Economic Dynamics
in: IWH Discussion Papers, No. 4, 2020
In this paper, a simple integrated model for the joint assessment of epidemic and economic dynamics is developed. The model can be used to discuss mitigation policies like shutdown and testing. Since epidemics cause output losses due to a reduced labor force, temporarily reducing economic activity in order to prevent future losses can be welfare enhancing. Mitigation policies help to keep the number of people requiring intensive medical care below the capacity of the health system. The optimal policy is a mixture of temporary partial shutdown and intensive testing and isolation of infectious persons for an extended period of time.
Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions
in: NBER Working Paper No. 26606, 2020
This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.