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.

Ihr Kontakt

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

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

Katja Heinisch Rolf Scheufele

in: Empirical Economics , im Erscheinen

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

Is East Germany Catching Up? A Time Series Perspective

Bernd Aumann Rolf Scheufele

in: Post-Communist Economies , 2010

Abstract

This article assesses whether the economy of East Germany is catching up with the West German region in terms of welfare. While the primary measure for convergence and catching up is per capita output, we also look at other macroeconomic indicators such as unemployment rates, wage rates and production levels in the manufacturing sector. In contrast to existing studies of convergence between regions of the reunified Germany, our approach is based purely upon the time series dimension and is thus directly focused on the catching up process in East Germany as a region. Our testing set-up includes standard ADF unit root tests as well as unit root tests that endogenously allow for a break in the deterministic component of the process. We find evidence of catching up for East Germany for most of the indicators. However, the convergence speed is slow, and thus it can be expected that the catching up process will take further decades until the regional gap is closed.

Publikation lesen

The Financial Crisis from a Forecaster's Perspective

Katja Drechsel Rolf Scheufele

in: Kredit und Kapital , Nr. 1, 2012

Abstract

This paper analyses the recession in 2008/2009 in Germany. This recession is very different from previous recessions in particular regarding their causes and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts withthe best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts in the crisis compared to indicator forecasts is small.

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Arbeitspapiere

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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

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.

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

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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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

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.

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