IDEAS home Printed from https://ideas.repec.org/p/ags/hwwadp/26321.html
   My bibliography  Save this paper

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

Author

Listed:
  • Dreger, Christian
  • Schumacher, Christian

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.

Suggested Citation

  • Dreger, Christian & Schumacher, Christian, 2002. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Discussion Paper Series 26321, Hamburg Institute of International Economics.
    • Handle: RePEc:ags:hwwadp:26321
    DOI: 10.22004/ag.econ.26321
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/26321/files/dp020199.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.26321?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    3. Fritsche Ulrich & Stephan Sabine, 2002. "Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(3), pages 289-315, June.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. 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 254-259, April.
    6. 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.
    7. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    10. Michael Funke & Harm Bandholz, 2003. "In search of leading indicators of economic activity in Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 277-297.
    11. Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario & Altissimo, Filippo & Cristadoro, Riccardo & Veronese, Giovanni & Bassanetti, Antonio, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. Michael Scharnagl, 1998. "The stability of German money demand: Not just a myth," Empirical Economics, Springer, vol. 23(3), pages 355-370.
    14. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
    15. Breitung, Jörg & Jagodzinski, Doris, 2002. "Prognoseeigenschaften alternativer Indikatoren für die Konjunkturentwicklung in Deutschland," SFB 373 Discussion Papers 2002,36, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    16. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    17. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    18. Peter Grasmann & Filip Keereman, 2001. "An indicator-based short-term forecast for quarterly GDP in the euro area," European Economy - Economic Papers 2008 - 2015 154, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.
    2. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    3. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    4. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    5. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    6. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    7. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    8. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    9. Van Nieuwenhuyze, Christophe & Benk, Szilard & Rünstler, Gerhard & Cristadoro, Riccardo & Den Reijer, Ard & Jakaitiene, Audrone & Jelonek, Piotr & Rua, António & Ruth, Karsten & Barhoumi, Karim, 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
    10. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    11. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    12. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    13. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    14. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    15. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Annual Conference Volume (Discontinued), in: Christopher Kent & David Norman (ed.),The Changing Nature of the Business Cycle, Reserve Bank of Australia.
    16. David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
    17. 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.
    18. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    19. repec:zbw:bofitp:2006_006 is not listed on IDEAS
    20. Declan Curran & Michael Funke, 2006. "Taking the Temperature - Forecasting GDP Growth for Mainland China," Quantitative Macroeconomics Working Papers 20606, Hamburg University, Department of Economics.
    21. Domenico Giannone & Troy D. Matheson, 2007. "A New Core Inflation Indicator for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 145-180, December.

    More about this item

    Keywords

    International Development;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:hwwadp:26321. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/hwwaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.