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Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP

  • Christian Schumacher

    ()

    (Deutsche Bundesbank)

This paper provides a review of the recent literature concerned with large factor models as forecast devices.We focus on factor models that account for mixed-frequency data and missing observations at the end of the sample. These are data irregularities applied forecasters have to cope with in real time. To extract the factors from the irregular data, special factor estimation techniques are necessary, expanding on the standard approaches for balanced data such as principal components (PC). The estimation methods include variants of the Expectation-Maximisation (EM) algorithm together with PC and factor estimation using state-space models. Given the estimated factors, forecasts can be obtained from bridge equations, mixed-data sampling (MIDAS) regressions and the Kalman smoother applied to fully-fledged factor models in state-space form. Empirical applications for German GDP growth often find that forecasts based on factor models are informative only a few months ahead compared to naive benchmarks. Thus, these models can be regarded as short-term forecast tools only. However, the factor models estimated on mixed-frequency data with missing observations tend to outperform factor models based on balanced data time-aggregated from high-frequency data.

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Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 231 (2011)
Issue (Month): 1 (February)
Pages: 28-49

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  1. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).
  2. Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
  3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  4. Konstantin A. Kholodilin & Boriss Siliverstovs, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 226(3), pages 234-259, May.
  5. Karim Barhoumi & Szilard Benk & Riccardo Cristadoro & Ard Den Reijer & Audrone Jakaitiene & Piotr Jelonek & António Rua & Gerhard Rünstler & Karsten Ruth & Christophe Van Nieuwenhuyze, 2008. "Short-term forecasting of GDP using large monthly datasets - a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
  6. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
  7. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  8. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
  9. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2007. "Regression Models with Mixed Sampling Frequencies," University of Cyprus Working Papers in Economics 8-2007, University of Cyprus Department of Economics.
  10. Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2003. "Interpolation and backdating with a large information set," Working Paper Series 0252, European Central Bank.
  11. Antonello D’ Agostino & Domenico Giannone, 2012. "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 306-326, 04.
  12. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
  13. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
  14. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2007. "New Eurocoin: Tracking Economic Growth in Real Time," Temi di discussione (Economic working papers) 631, Bank of Italy, Economic Research and International Relations Area.
  15. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank, Research Centre.
  16. 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.
  17. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
  18. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  19. Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series 1189, European Central Bank.
  20. Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
  21. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
  22. Dreger, Christian & Schumacher, Christian, 2002. "Estimating large-scale factor models for economic activity in Germany : do they outperform simpler models?," HWWA Discussion Papers 199, Hamburg Institute of International Economics (HWWA).
  23. Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
  24. Guillaume Chevillon & David F. Hendry, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Papers 2004-W12, Economics Group, Nuffield College, University of Oxford.
  25. Camacho, Maximo & Pérez-Quirós, Gabriel, 2009. "Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth," CEPR Discussion Papers 7343, C.E.P.R. Discussion Papers.
  26. Arvid Raknerud & Terje Skjerpen & Anders Rygh Swensen, 2010. "Forecasting key macroeconomic variables from a large number of predictors: a state space approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 367-387.
  27. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  28. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
  29. Boriss Siliverstovs & Kinstantin Kholodilim, 2009. "On selection of components for a diffusion index model: it's not the size, it's how you use it," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1249-1254.
  30. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  31. Maximo Camacho & Gabriel Perez Quiros, 2011. "Spain‐Sting: Spain Short‐Term Indicator Of Growth," Manchester School, University of Manchester, vol. 79(s1), pages 594-616, 06.
  32. Anindya Banerjee & Massimiliano Marcellino, 2003. "Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?," Working Papers 236, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  33. Boriss Siliverstovs & Konstantin A. Kholodilin, 2010. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," KOF Working papers 10-251, KOF Swiss Economic Institute, ETH Zurich.
  34. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638009, HAL.
  35. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.
  36. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
  37. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
  38. Banbura, Marta & Rünstler, Gerhard, 2007. "A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 0751, European Central Bank.
  39. Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
  40. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
  41. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  42. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-22.
  43. 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.
  44. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  45. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
  46. Wohlrabe, Klaus, 2009. "Forecasting with mixed-frequency time series models," Munich Dissertations in Economics 9681, University of Munich, Department of Economics.
  47. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2003. "Leading Indicators for Euro Area Inflation and GDP Growth," CEPR Discussion Papers 3893, C.E.P.R. Discussion Papers.
  48. Schumacher, Christian, 2010. "Factor forecasting using international targeted predictors: The case of German GDP," Economics Letters, Elsevier, vol. 107(2), pages 95-98, May.
  49. 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.
  50. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
  51. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  52. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
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