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Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?

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Dreger, Christian
Schumacher, Christian

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Abstract

This paper discusses a large-scale factor model for the German economy. Following the recent literature, a data set of 121 time series is used via principal component analysis to determine the factors, which enter a dynamic model for German GDP. The model is compared with alternative univariate and multivariate models. These models are based on regression techniques and considerably smaller data sets. Out-of-sample forecasts show that the prediction errors of the factor model are smaller than the errors of the rival models. However, these advantages are not statistically significant, as a test for equal forecast accuracy shows. Therefore, the efficiency gains of using a large data set with this kind of factor models seem to be limited. Diese Arbeit diskutiert ein großes Faktorenmodell für die deutsche Wirtschaft. Der jüngeren Literatur folgend werden aus einem umfangreichen Datensatz von 121 Zeitrehen mit einer Hauptkomponentenanalyse gemeinsame Faktoren extrahiert, welche in ein dynamisches Modell zur Erklärung des deutschen Bruttoinlandsprodukts eingehen. Das Modell wird mit alternativen univariaten und multivariaten Modellen verglichen, die auf Regressionsansätzen und deutlich kleineren Datensätzen beruhen. Vergleiche von Pronosen außerhalb des Schätzzeitraums zeigen, dass die Prognosefehler des großen Faktenmodells kleiner als bei den alternativen Modellen sind. Jedoch sind diese emprischen Vorteile nicht statistisch signifikant, wie Tests auf paarweise Gleichheit der Prognosegüte zeigen. Demzufolge scheinen die Effizienzvorteile des auf einem großen Datensatz beruhenden Faktorenmodells lediglich gering zu sein.

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Paper provided by Hamburg Institute of International Economics in its series Discussion Paper Series with number 26321.

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Date of creation: 2002
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Handle: RePEc:ags:hiiedp:26321

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Related research
Keywords: Factor models; Principal components; forecasting accuracy; International Development; E32; C51; C43;

References listed on IDEAS
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  1. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Michael Scharnagl, 1998. "The stability of German money demand: Not just a myth," Empirical Economics, Springer, vol. 23(3), pages 355-370. [Downloadable!] (restricted)
  3. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin Weale, 1999. "An Automatic Leading Indicator of Economic Activity: Forecasting GDP growth for European Countries," NIESR Discussion Papers 149, National Institute of Economic and Social Research. [Downloadable!]
    Other versions:
  4. Bondonio, Daniele, 2002. "Evaluating the Employment Impact of Business Incentive Programs in EU Disadvantaged Areas. A case from Northern Italy," P.O.L.I.S. department's Working Papers 27, Department of Public Policy and Public Choice - POLIS. [Downloadable!]
  5. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  6. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  7. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September. [Downloadable!] (restricted)
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  8. Cochrane, John H., 1998. "What do the VARs mean? Measuring the output effects of monetary policy," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 277-300, April. [Downloadable!] (restricted)
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  9. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  10. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May. [Downloadable!] (restricted)
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  13. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  14. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007. [Downloadable!]
  2. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research. [Downloadable!]
    Other versions:
  3. Rangan Gupta & Alain Kabundi, 2008. "A Dynamic Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 200815, University of Pretoria, Department of Economics. [Downloadable!]
  4. Konstantin A. Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2007. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Discussion Papers of DIW Berlin 664, DIW Berlin, German Institute for Economic Research. [Downloadable!]
    Other versions:
  5. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka. [Downloadable!]
  6. Christophe Van Nieuwenhuyze, 2006. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts," Research series 200603-2, National Bank of Belgium. [Downloadable!]
  7. A.H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department. [Downloadable!]
  8. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany. [Downloadable!]
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