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19.09.2019 • 19/2019
Long-term effects of privatisation in eastern Germany: award-winning US economist begins large-scale research project at the IWH
It is one of the most prestigious awards in the German scientific community: the Max Planck-Humboldt Research Award 2019 endowed with €1.5 million goes to Ufuk Akcigit, Professor of Economics at the University of Chicago. At the Halle Institute for Economic Research (IWH), Akcigit aims to use innovative methods to investigate why the economy in eastern Germany is still lagging behind that in western Germany – and what role the privatisation process 30 years ago played in this.
Reint E. Gropp
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1st IWH-FIN-FIRE Workshop on Challenges to Financial Stability
Annika Bacher, Lena Tonzer
Wirtschaft im Wandel,
No. 5,
2015
Abstract
Im Rahmen des Workshops tauschten sich internationale Teilnehmerinnen und Teilnehmer über aktuelle Forschungspapiere rund um das Thema „Challenges to Financial Stability“ aus. Im Wesentlichen diente der Workshop als Plattform, um Änderungen in den regulatorischen Rahmenbedingungen des Finanzsektors und die daraus resultierenden Einflüsse auf die Finanzstabilität bzw. die Konsequenzen für die Realwirtschaft zu diskutieren.
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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Is there a Superior Distance Function for Matching in Small Samples?
Eva Dettmann, Claudia Becker, Christian Schmeißer
Abstract
The study contributes to the development of ’standards’ for the application of matching algorithms in empirical evaluation studies. The focus is on the first step of the matching procedure, the choice of an appropriate distance function. Supplementary o most former studies, the simulation is strongly based on empirical evaluation ituations. This reality orientation induces the focus on small samples. Furthermore, ariables with different scale levels must be considered explicitly in the matching rocess. The choice of the analysed distance functions is determined by the results of former theoretical studies and recommendations in the empirical literature. Thus, in the simulation, two balancing scores (the propensity score and the index score) and the Mahalanobis distance are considered. Additionally, aggregated statistical distance functions not yet used for empirical evaluation are included. The matching outcomes are compared using non-parametrical scale-specific tests for identical distributions of the characteristics in the treatment and the control groups. The simulation results show that, in small samples, aggregated statistical distance functions are the better
choice for summarising similarities in differently scaled variables compared to the
commonly used measures.
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Estimation Uncertainty in Credit Risk Assessment: Comparison of Credit Risk Using Bootstrapping and an Asymptotic Approach
Henry Dannenberg
IWH Discussion Papers,
No. 3,
2009
Abstract
For credit risk assessment, probability of default and correlation have to be estimated simultaneously. However, these estimates are uncertain. To assess this uncertainty the literature has discussed the use of asymptotic confidence regions. This kind of region though needs a long credit history for exact assessment. An alternative method to generate a confidence region for a short credit history is bootstrapping. Hence, it could be more appropriate to assess estimation uncertainty with bootstrapping than with asymptotic methods if only a short credit history is available. Based on a simulation study, it is analyzed how many periods should be available for assessing credit risk – taking account of estimation uncertainty – if bootstrapping and a Wald confidence region shall achieve similar results. This article shows that more than 100 cycles have to be available for similar results.
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