Dr. Rolf Scheufele

Dr. Rolf Scheufele
Aktuelle Position

seit 3/12

Research Affiliate

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 3/12

Wirtschaftswissenschaftler

Schweizerische Nationalbank

Forschungsschwerpunkte

  • angewandte Ökonometrie
  • makroökonomische Modellierung

Rolf Scheufele ist seit März 2012 Research Affiliate am IWH. Seine Forschungsinteressen liegen auf dem Gebiet der angewandten Ökonometrie und der makroökonometrischen Modellierung.

Rolf Scheufele arbeitet bei der Schweizerischen Nationalbank. Zuvor war er am IWH tätig.

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Dr. Rolf Scheufele
Dr. Rolf Scheufele
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Publikationen

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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: 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

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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.

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Arbeitspapiere

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Should We Trust in Leading Indicators? Evidence from the Recent Recession

Katja Drechsel Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 10, 2010

Abstract

The paper analyzes leading indicators for GDP and industrial production in Germany. We focus on the performance of single and pooled leading indicators during the pre-crisis and crisis period using various weighting schemes. Pairwise and joint significant tests are used to evaluate single indicator as well as forecast combination methods. In addition, we use an end-of-sample instability test to investigate the stability of forecasting models during the recent financial crisis. We find in general that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis the relative performance of many leading indicator models increased. At short horizons, survey indicators perform best, while at longer horizons financial indicators, such as term spreads and risk spreads, improve relative to the benchmark.

Publikation lesen

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Is East Germany Catching Up? A Time Series Perspective

Bernd Aumann Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 14, 2009

Abstract

This paper 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 manufacturingsector. In contrast to existing studies of convergence between regions of reunified Germany, our approach is purely based upon the time series dimension and is thus directly focused on the catching up process in East Germany as a region. Our testing setup 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. In our analysis, we find evidence of catching up for East Germany for most of the indicators. However, 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

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Evaluating the German (New Keynesian) Phillips Curve

Rolf Scheufele

in: IWH-Diskussionspapiere, Nr. 10, 2008

Abstract

This paper evaluates the New Keynesian Phillips Curve (NKPC) and its hybrid variant within a limited information framework for Germany. The main interest rests on the average frequency of price re-optimization of firms. We use the labor income share as the driving variable and consider a source of real rigidity by allowing for a fixed firm-specific capital stock. A GMM estimation strategy is employed as well as an identification robust method that is based upon the Anderson-Rubin statistic. We find out that the German Phillips Curve is purely forward looking. Moreover, our point estimates are consistent with the view that firms re-optimize prices every two to three quarters. While these estimates seem plausible from an economic point of view, the uncertainties around these estimates are very large and also consistent with perfect nominal price rigidity where firms never re-optimize prices. This analysis also offers some explanations why previous results for the German NKPC based on GMM differ considerably. First, standard GMM results are very sensitive to the way how orthogonality conditions are formulated. Additionally, model misspecifications may be left undetected by conventional J tests. Taken together, this analysis points out the need for identification robust methods to get reliable estimates for the NKPC.

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