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

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  • Ziegler, Christina
  • Eickmeier, Sandra

Abstract

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. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2006,42.

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Date of creation: 2006
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Handle: RePEc:zbw:bubdp1:5170

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Keywords: Factor models; forecasting; meta-analysis;

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References

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  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  2. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  3. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
  4. von Kalckreuth, Ulf, 2005. "A "wreckers theory" of financial distress," Discussion Paper Series 1: Economic Studies 2005,40, Deutsche Bundesbank, Research Centre.
  5. Slacalek, Jirka & Fritsche, Ulrich & Dovern, Jonas & Döpke, Jörg, 2005. "European inflation expectations dynamics," Discussion Paper Series 1: Economic Studies 2005,37, Deutsche Bundesbank, Research Centre.
  6. Fecht, Falko & Huang, Kevin & Martin, Antoine, 2005. "Financial intermediaries, markets and growth," Discussion Paper Series 1: Economic Studies 2005,03, Deutsche Bundesbank, Research Centre.
  7. Koetter, Michael & Bos, Jaap W. B. & Heid, Frank & Kool, Clemens J. M. & Kolari, James W. & Porath, Daniel, 2005. "Accounting for distress in bank mergers," Discussion Paper Series 2: Banking and Financial Studies 2005,09, Deutsche Bundesbank, Research Centre.
  8. William T. Gavin & Kevin L. Kliesen, 2006. "Forecasting inflation and output: comparing data-rich models with simple rules," Working Papers 2006-054, Federal Reserve Bank of St. Louis.
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Citations

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Cited by:
  1. 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.
  2. William T. Gavin & Kevin L. Kliesen, 2006. "Forecasting inflation and output: comparing data-rich models with simple rules," Working Papers 2006-054, Federal Reserve Bank of St. Louis.
  3. Claudia Godbout & Marco J. Lombardi, 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
  4. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, . "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
  5. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
  6. 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.
  7. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, Ifo Institute for Economic Research at the University of Munich, number 36, December.
  8. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  9. Marco J. Lombardi & Philipp Maier, 2011. "Forecasting economic growth in the euro area during the great moderation and the great recession," Working Paper Series 1379, European Central Bank.
  10. 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.
  11. Michal Brzoza-Brzezina & Jacek Kotlowski, 2009. "Estimating pure inflation in the Polish economy," Working Papers 37, Department of Applied Econometrics, Warsaw School of Economics.
  12. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," BORRADORES DE ECONOMIA 007996, BANCO DE LA REPÚBLICA.
  13. Ard den Reijer, 2007. "Identifying Regional and Sectoral Dynamics of the Dutch Staffing Labour Cycle," DNB Working Papers 153, Netherlands Central Bank, Research Department.
  14. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.
  15. Eliana González, . "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 643, Banco de la Republica de Colombia.

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