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Gerhard Rünstler

Research

Division

Monetary Policy Research

Current Position

Senior Lead Economist

Fields of interest

Macroeconomics and Monetary Economics,Mathematical and Quantitative Methods

Email

gerhard.ruenstler@ecb.europa.eu

Education
1990-1992

Postgraduate Studies in Econometrics, Institute for Advanced Studies, Vienna

1982-1988

PhD Social Sciences, University of Graz

1980-1986

MSc Mathematics, Technical University Graz

Professional experience
2009-2012

Austrian Institute for Economic Research, 2009-2012

1999-2003

Economist, European Central Bank, 1999-2003

2003

Principal Economist, European Central Bank, since 2003

1998-1999

Economist, Austrian National Bank, 1998-1999

1992-1998

Assistant Professor at Institute for Advanced Studies, Vienna, 1992-1998

Awards
2009

Isaac Kerstenetzky Award, CIRET conference 2009, New York

Teaching experience
2009

International Economics at University of Applied Sciences Vienna 2009

1995-1998

Econometrics I at Institute for Advanced Studies 1995-1998

1996-1998

Tutorials in time series analysis at Vienna Business School 1996-1998

1 September 2002
WORKING PAPER SERIES - No. 182
Details
Abstract
The paper investigates real-time output gap estimates for the euro artea obtained from various unobserved components (UOC) models. Based on a state space modelling framework, three criteria are used to evaluate real-time estimates, I.e. standard errors, unbiasedness and conditional inflation forecasts. Real time estimates from univariate moving average filters and from bivariate UOC models based on output and inflation are found to be rather uninformative. Extended models, which employ the information from cyclical indicators and factor inputs, however, improve substantially upon the former models in all criteria. The pessimism on the reliability of real-time output gap estimates expressed in earlier literature may therefore be overstated.
JEL Code
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
1 September 2003
WORKING PAPER SERIES - No. 276
Details
Abstract
The first official data releases of quarterly real GDP for the euro area are published about eight weeks after the end of the reference quarters. Meanwhile, ongoing economic developments must be assessed from various, more readily available, monthly indicators. We examine in the context of univariate forecasting equations to what extent monthly indicators provide useful information for predicting euro area real GDP growth over the current and the next quarter. In particular, we investigate the performance of the equations under the case that the monthly indicators are only partially available within the quarter. For this purpose, we use time series models to forecast the missing observations of monthly indicators. We then examine GDP forecasts under different amounts of monthly information. We find that already a limited amount of monthly information improves the predictions for current-quarter GDP growth to a considerable extent, compared with ARIMA forecasts.
JEL Code
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
23 May 2007
WORKING PAPER SERIES - No. 751
Details
Abstract
We derive forecast weights and uncertainty measures for assessing the role of individual series in a dynamic factor model (DFM) to forecast euro area GDP from monthly indicators. The use of the Kalman filter allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts. However, this is discovered only, if their more timely publication is properly taken into account. Differences in publication lags play a very important role and should be considered in forecast evaluation.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
8 May 2008
OCCASIONAL PAPER SERIES - No. 84
Details
Abstract
This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
28 October 2008
WORKING PAPER SERIES - No. 953
Details
Abstract
We estimate and forecast growth in euro area monthly GDP and its components from a dynamic factor model due to Doz et al. (2005), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting together with cross-equation accounting identities. A pseudo real-time forecasting exercise indicates that the model outperforms various benchmarks, such as quarterly time series models and bridge equations in forecasting growth in quarterly GDP and its components.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
28 October 2008
WORKING PAPER SERIES - No. 949
Details
Abstract
Global financial integration unlocks a huge potential for international risk sharing. We examine the degree to which international equity holdings act as a risk sharing device in industrial and emerging economies. We split equity returns into investment income (dividend distribution) and capital gains to investigate which of the two channels delivers the largest potential for risk sharing. Our evidence suggests that net capital gains are a more potent channel of risk sharing. They behave in a countercyclical way, that is they tend to be positive (negative) when the domestic economy is growing more slowly (rapidly) than the rest of the world. Countries with more countercyclical net capital gains experience improved consumption risk sharing. The empirical analysis furthermore suggests that these risk sharing properties of net capital gains have increased through time, in particular in the 1990s and early-2000s, on the back of a declining equity home bias and financial market deepening.
JEL Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
3 March 2016
WORKING PAPER SERIES - No. 1887
Details
Abstract
I estimate network dependence effects in the euro area unsecured overnight interbank market during the ?financial crisis. I use linear spatial regressions to estimate the dependence of individual banks?trading volumes (and interest rates) on the trading volumes (and interest rates) of their network neighbours. Neighbours are de?fined from past trading relations. I ?find that banks?net lending volumes and lending-borrowing interest rate spread depend negatively on their neighbours? respective outcomes. By contrast, there arise positive effects for total trading volume and borrowing rates. Overall, however, these effects are small and signi?ficant only in periods of market turmoil or of major policy interventions. The results suggest that neighbours act as a buffer in absorbing idiosyncratic liquidity shocks.
JEL Code
C21 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions
E42 : Macroeconomics and Monetary Economics→Money and Interest Rates→Monetary Systems, Standards, Regimes, Government and the Monetary System, Payment Systems
20 April 2016
WORKING PAPER SERIES - No. 1893
Details
Abstract
Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and Least Angle Regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
7 June 2016
WORKING PAPER SERIES - No. 1915
Details
Abstract
We use multivariate unobserved components models to estimate trend and cyclical components in GDP, credit volumes and house prices for the U.S. and the five largest European economies. With the exception of Germany, we find large and long cycles in credit and house prices, which are highly correlated with a medium-term component in GDP cycles. Differences across countries in the length and size of cycles appear to be related to the properties of national housing markets. The precision of pseudo real-time estimates of credit and house price cycles is roughly comparable to that of GDP cycles.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
31 August 2016
RESEARCH BULLETIN - No. 26
Details
Abstract
One foundation for developing new macroprudential policy in addition to traditional macroeconomic stabilisation policy is that financial cycles differ from business cycles. This article identifies properties of credit and housing cycles, shows how they relate to GDP cycles, and compares the reliability of real-time estimates.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
16 May 2017
OCCASIONAL PAPER SERIES - No. 191
Details
Abstract
This paper investigates the interrelations between monetary macro- and microprudential policies. It first provides an overview of the three policies, starting with their main instruments and objectives. Monetary policy aims at maintaining price stability and promoting balanced economic growth, macroprudential policies aim at safeguarding the stability of the overall financial system, while microprudential policies contribute to the safety and soundness of individual entities. Subsequently, the paper provides a simplified description of their respective transmission mechanisms and analyses the interactions between them. A conceptual framework is first presented on the basis of which the analysis of the interactions across the different policies can be demonstrated in a stylised manner. These stylised descriptions are then further complemented by model-based simulations illustrating the significant complementarities and interactions between them. Finally, the paper concludes that from a conceptual point of view there are numerous areas of interaction between the policies. These create scope for synergies, which can be reaped by sharing information and expertise across the various policy areas.
JEL Code
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
9 January 2018
OCCASIONAL PAPER SERIES - No. 205
Details
Abstract
This paper studies the cyclical properties of real GDP, house prices, credit, and nominal liquid financial assets in 17 EU countries, by applying several methods to extract cycles. The estimates confirm earlier findings of large medium-term cycles in credit volumes and house prices. GDP appears to be subject to fluctuations at both business-cycle and medium-term frequencies, and GDP fluctuations at medium-term frequencies are strongly correlated with cycles in credit and house prices. Cycles in equity prices and long-term interest rates are considerably shorter than those in credit and house prices and have little in common with the latter. Credit and house price cycles are weakly synchronous across countries and their volatilities vary widely – these differences may be related to the structural properties of housing and mortgage markets. Finally, DSGE models can replicate the volatility of cycles in house and equity prices, but not the persistence of house price cycles.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
6 January 2020
WORKING PAPER SERIES - No. 2353
Details
Abstract
We study the identification of policy shocks in Bayesian proxy VARs for the case that the instrument consists of sparse qualitative observations indicating the signs of certain shocks. We propose two identification schemes, i.e. linear discriminant analysis and a non-parametric sign concordance criterion. Monte Carlo simulations suggest that these provide more accurate confidence bounds than standard proxy VARs and are more efficient than local projections. Our application to U.S. macroprudential policies finds persistent effects of capital requirements and mortgage underwriting standards on credit volumes and house prices together with moderate effects on GDP and inflation.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G38 : Financial Economics→Corporate Finance and Governance→Government Policy and Regulation
Network
Research Task Force (RTF)
15 June 2020
WORKING PAPER SERIES - No. 2421
Details
Abstract
We study state dependence in the impact of monetary policy shocks over the leverage cycle for a panel of 10 euro area countries. We use a Bayesian Threshold Panel SVAR with regime classifications based on credit and house prices cycles. We find that monetary policy shocks trigger a smaller response of GDP, but a larger response of inflation during low states of the cycle. The shift in the inflation-output trade-off may result from higher macro-economic uncertainty in low leverage states. For an alternative regime classification based on turning points we find larger effects on GDP during contractions.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
Network
Research Task Force (RTF)
28 May 2021
WORKING PAPER SERIES - No. 2559
Details
Abstract
Since the global financial crises, many countries have implemented macroprudential policies with the aim to render the financial system more resilient to shocks and limit the procyclicality of the financial system. We present theoretical and empirical evidence on the effectiveness of macroprudential policy, on both, financial stability and economic growth focussing on capital measures and borrower-based measures.
JEL Code
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
Network
Discussion papers
2018
Journal of Applied Econometrics 33(2), 212-226
  • G. Rünstler and M. Vlekke
2016
Journal of Computational Economics and Econometrics 6(3), 294-314
  • G Rünstler
2016
Hillebrand and Koopman, Advances in Econometrics, Vol 35, Emerald Publishing
  • G. Rünstler
2011
Econometrics Journal 14(1), C25-C44.
  • Angelini, E., G. Camba-Mendez, D. Giannone, L. Reichlin, G. Rünstler
2011
International Journal of Forecasting 27(2), 333-46.
  • M. Banbura and G. Rünstler
2010
Journal of Business Cycle Measurement and Analysis 2010(1), 5-26
  • E. Angelini, M. Banbura, G. Rünstler
2010
Journal of Forecasting 28(7), 595-611
  • G. Rünstler et al.
2004
Econometrics Journal 7, 232-48.
  • G. Rünstler
2000
Applied Economics Letters 7(1), 25-28
The dynamic effects of aggregate supply and demand disturbances: further evidence
  • H. Hofer, G. Rünstler, T. Url
1999
Applied Financial Economics 9, 101-108
  • A. Jumah, S. Karbuz, G. Rünstler
2016
Advances in Econometrics Vol 25
  • Ruenstler, G.