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Empirical Economics

, Volume 44, Issue 2, pp 435–453 | Cite as

Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets

Article

Abstract

We compare the factor forecasting performance of nested specifications of the generalized factor model based on various configurations of a large macroeconomic data set. The forecast simulation design involves in-sample model selection, factor estimation, parameter estimation and, finally, generating factor forecasts and factor augmented autoregressive forecasts. To empirically determine the importance of the size and the structure of the data set, we run the forecast simulation design for different configurations of the data set. We compare the factor model diagnostics of each specification and data configuration with the corresponding forecast performance. The results favour the factor structure as the specification that imposes the factor structure to the least extent and, hence, is allowed most flexibility to adapt to the data, is significantly being outperformed. Moreover, the results show that size matters as though smaller macroeconomic data sets exhibit stronger coherence, the factors being well fit, however, generally do not show improved forecasting performance.

Keywords

Factor models Macroeconomic forecasting Leading indicators 

JEL Classification

C43 C51 E32 

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Supplementary material

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References

  1. Altissimo F, Cristadoro R, Forni M, Lippi M, Veronese G (2010) New Eurocoin: tracking economic growth in real time. Rev Econ Stat 92(4): 1024–1034CrossRefGoogle Scholar
  2. Bai J, Ng S (2002) Determining the number of factors in approximate factor models. Econometrica 70: 191–221CrossRefGoogle Scholar
  3. Bai J, Ng S (2008) Forecasting economic time series using targeted predictors. J Econom 146(2): 304–317CrossRefGoogle Scholar
  4. Bates J, Granger C (1969) The combination of forecasts. Oper Res Q 20: 451–468CrossRefGoogle Scholar
  5. Boivin J, Ng S (2005) Understanding and comparing factor-based forecasts. Int J Central Bank 1: 117–151Google Scholar
  6. Boivin J, Ng S (2006) Are more data always better for factor analysis. J Econom 132: 169–194CrossRefGoogle Scholar
  7. Brillinger D (1981) Time series: data analysis and theory. Holdan-Day, San Francisco ISBN 0-89871-501-6Google Scholar
  8. Caggiano G, Kapetanios G, Labhard V (2011) Are more data always better for factor analysis? Results for the Euro area, the six largest Euro area countries and the UK. J Forecast 30(8): 736–752CrossRefGoogle Scholar
  9. Clements M, Hendry D (1998) Forecasting economic time series. Cambridge University Press, Cambridge ISBN 0-19-828700-3CrossRefGoogle Scholar
  10. D’Agostino A, Giannone D (2012) Comparing alternative predictors based on large-panel dynamic factor models. Oxford Bull Econ Stat 74(2): 306–326CrossRefGoogle Scholar
  11. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2): 407–499CrossRefGoogle Scholar
  12. Eickmeier S, Ziegler C (2008) How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. J Forecast 27(3): 237–265CrossRefGoogle Scholar
  13. Forni M, Lippi M (2001) The generalized dynamic factor model: representation theory. Econ Theory 17: 1113–1141Google Scholar
  14. Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized factor model: identification and estimation. Rev Econ Stat 82(4): 540–554CrossRefGoogle Scholar
  15. Forni M, Highfield F, Palm F, Zellner A (2001) Coincident and leading indicators for the Euro area. Econ J 111: 62–85CrossRefGoogle Scholar
  16. Forni M, Hallin M, Lippi M, Reichlin L (2004) The generalized factor model: consistency and rates. J Econom 119(2): 231–255CrossRefGoogle Scholar
  17. Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471): 830–840CrossRefGoogle Scholar
  18. Garcia-Ferrer A, Forni M, Hallin M, Lippi M, Reichlin L (1987) Macroeconomic forecasting using pooled international data. J Bus Econ Stat 5: 53–67Google Scholar
  19. Giacomini R, White H (2006) Tests of conditional predictive ability. Econometrica 74(6): 1545–1578CrossRefGoogle Scholar
  20. Gómez V, Maravall A (1996) Programs tramo and seats, instructions for the user (beta version September 1996). Working Paper 9628, Bank of SpainGoogle Scholar
  21. Hoogstrate A, Pfann G, Palm F (2000) Pooling in dynamic panel data models. J Bus Econ Stat 18: 274–283Google Scholar
  22. Jacobs J, Otter P, den Reijer A (2012) Information, data dimension and factor structure. J Multivar Anal 106: 80–91CrossRefGoogle Scholar
  23. Palm F, Zellner A (1992) To combine or not to combine? Issues of combining forecasts. J Forecast 11: 687–701CrossRefGoogle Scholar
  24. Priestley M (1982) Spectral analysis and time series. ISBN 0-12-564922-3Google Scholar
  25. Rünstler G, Benk S, Cristadoro R, Den Reijer A, Jakaitiene A, Jelonek P, Rua A, Ruth K, Van Nieuwenhuyze C (2009) Short-term forecasting of GDP using large scale monthly datasets: a pseudo real-time forecast evaluation exercise. J Forecast 28(7): 595–611CrossRefGoogle Scholar
  26. Schumacher C (2007) Forecasting German GDP using alternative factor models based on large datasets. J Forecast 26(4): 271–302CrossRefGoogle Scholar
  27. Schumacher C (2010) Factor forecasting using international targeted predictors: the case of German GDP. Econ Lett 107(2): 95–98CrossRefGoogle Scholar
  28. Stock J, Watson M (2002a) Forecasting using principal components from a large number of time predictors. J Am Stat Assoc 97: 1167–1179CrossRefGoogle Scholar
  29. Stock J, Watson M (2002b) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20: 147–162CrossRefGoogle Scholar
  30. Svensson L (2005) Monetary policy with judgment: forecast targeting. Int J Central Bank 1(1): 1–54Google Scholar
  31. Van Els P, Vlaar P (1996) Morkmon iii, een geactualiseerde versie van het macro-economische beleidsmodel van de nederlandsche bank. Research Memorandum WO&E 471, De Nederlandsche BankGoogle Scholar
  32. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc B 67(2): 301–320CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Monetary Policy DepartmentSveriges RiksbankStockholmSweden

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