25 Jahre IWH

Dr. Rolf Scheufele

Dr. Rolf Scheufele
Aktuelle Position

seit 3/12

Research Affiliate

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 3/12

Schweizerische Nationalbank

Forschungsschwerpunkte

  • angewandte Ökonometrie
  • makroökonomische Modellierung

Dr. Rolf Scheufele arbeitet mit dem IWH in Forschungsprojekten zu Kurzfristprognose und DSGE-Modellierung zusammen. Vor seiner Tätigkeit bei der Schweizerischen Nationalbank war er einige Jahre in der Abteilung Makroökonomik des IWH tätig.

Seine Forschungsinteressen liegen auf dem Gebiet der angewandten Ökonometrie und der makroökonometrischen Modellierung.

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Dr. Rolf Scheufele
Dr. Rolf Scheufele
Mitglied - Abteilung Makroökonomik
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Publikationen

Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Katja Heinisch Rolf Scheufele

in: German Economic Review, im Erscheinen

Abstract

In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

Publikation lesen

Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Heinisch Rolf Scheufele

in: Empirical Economics, Nr. 2, 2018

Abstract

In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.

Publikation lesen

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Impulse Response Analysis in a Misspecified DSGE Model: A Comparison of Full and Limited Information Techniques

Sebastian Giesen Rolf Scheufele

in: Applied Economics Letters, Nr. 3, 2016

Abstract

In this article, we examine the effect of estimation biases – introduced by model misspecification – on the impulse responses analysis for dynamic stochastic general equilibrium (DSGE) models. Thereby, we use full and limited information estimators to estimate a misspecified DSGE model and calculate impulse response functions (IRFs) based on the estimated structural parameters. It turns out that IRFs based on full information techniques can be unreliable under misspecification.

Publikation lesen

Arbeitspapiere

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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Katja Heinisch Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 5, 2017
publiziert in: German Economic Review

Abstract

In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

Publikation lesen

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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

in: IWH-Diskussionspapiere, Nr. 8, 2013
publiziert in: Journal of Macroeconomics

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.

Publikation lesen

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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Drechsel Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 7, 2013
publiziert in: Empirical Economics

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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