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How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach


  • Ziegler, Christina
  • Eickmeier, Sandra


This paper surveys existing factor forecast applications for real economic activity and inflation by means of a meta-analysis and contributes to the current debate on the determinants of the forecast performance of large-scale dynamic factor models relative to other models. We find that, on average, factor forecasts are slightly better than other models' forecasts. In particular, factor models tend to outperform small-scale models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. Our results further suggest that factor forecasts are better for US than for UK macroeconomic variables, and that they are better for US than for euro-area output; however, there are no significant differences between the relative factor forecast performance for US and euro-area inflation. There is also some evidence that factor models are better suited to predict output at shorter forecast horizons than at longer horizons. These findings all relate to the forecasting environment (which cannot be influenced by the forecasters). Among the variables capturing the forecasting design (which can, by contrast, be influenced by the forecasters), the size of the dataset from which factors are extracted seems to positively affect the relative factor forecast performance. There is some evidence that quarterly data lend themselves better to factor forecasts than monthly data. Rolling forecasts are preferable to recursive forecasts. The factor estimation technique seems to matter as well. Other potential determinants - namely whether forecasters rely on a balanced or an unbalanced panel, whether restrictions implied by the factor structure are imposed in the forecasting equation or not and whether an iterated or a direct multi-step forecast is made - are found to be rather irrelevant. Moreover, we find no evidence that pre-selecting the variables to be included in the panel from which factors are extracted helped to improve factor forecasts in the past.

Suggested Citation

  • Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:5170

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    References listed on IDEAS

    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.
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    4. Slacalek, Jirka & Fritsche, Ulrich & Dovern, Jonas & Döpke, Jörg, 2005. "European inflation expectations dynamics," Discussion Paper Series 1: Economic Studies 2005,37, Deutsche Bundesbank.
    5. Falko Fecht & Kevin X. D. Huang & Antoine Martin, 2008. "Financial Intermediaries, Markets, and Growth," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(4), pages 701-720, June.
    6. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2007. "Learning, Structural Instability, and Present Value Calculations," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 253-288.
    7. Koetter, M. & Bos, J.W.B. & Heid, F. & Kolari, J.W. & Kool, C.J.M. & Porath, D., 2007. "Accounting for distress in bank mergers," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3200-3217, October.
    8. William T. Gavin & Kevin L. Kliesen, 2008. "Forecasting inflation and output: comparing data-rich models with simple rules," Review, Federal Reserve Bank of St. Louis, issue May, pages 175-192.
    9. Döpke, Jörg & Funke, Michael & Holly, Sean & Weber, Sebastian, 2005. "The cross-sectional dynamics of German business cycles: a bird's eye view," Discussion Paper Series 1: Economic Studies 2005,23, Deutsche Bundesbank.
    10. von Kalckreuth, Ulf, 2005. "A "wreckers theory" of financial distress," Discussion Paper Series 1: Economic Studies 2005,40, Deutsche Bundesbank.
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    Cited by:

    1. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    2. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," BORRADORES DE ECONOMIA 005273, BANCO DE LA REPÚBLICA.
    3. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    4. Lombardi, Marco J. & Godbout, Claudia, 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
    5. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 36, April.
    6. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    7. Michal Brzoza-Brzezina & Jacek Kotlowski, 2009. "Estimating pure inflation in the Polish economy," Working Papers 37, Department of Applied Econometrics, Warsaw School of Economics.
    8. Ard den Reijer, 2007. "Identifying Regional and Sectoral Dynamics of the Dutch Staffing Labour Cycle," DNB Working Papers 153, Netherlands Central Bank, Research Department.
    9. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," BORRADORES DE ECONOMIA 007996, BANCO DE LA REPÚBLICA.
    10. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. William T. Gavin & Kevin L. Kliesen, 2008. "Forecasting inflation and output: comparing data-rich models with simple rules," Review, Federal Reserve Bank of St. Louis, issue May, pages 175-192.
    12. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.
    13. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 53.
    14. Yin-Wong Cheung & Matthew S. Yiu & Kenneth K. Chow, 2009. "A Factor Analysis of Trade Integration: the Case of Asian and Oceanic Economies," Economie Internationale, CEPII research center, issue 119, pages 5-23.
    15. El-Shagi, Makram, 2009. "Inflation Expectations: Does the Market Beat Professional Forecasts?," IWH Discussion Papers 16/2009, Halle Institute for Economic Research (IWH).
    16. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.

    More about this item


    Factor models; forecasting; meta-analysis;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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