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Robust Approaches to Forecasting

Listed author(s):
  • Jennifer Castle
  • David Hendry
  • Michael P. Clements

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

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File URL: http://www.economics.ox.ac.uk/materials/papers/13237/paper697.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 697.

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Date of creation: 30 Jan 2014
Handle: RePEc:oxf:wpaper:697
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Web page: http://www.economics.ox.ac.uk/
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  1. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  2. Guillaume Chevillon, 2005. "Direct multi-step estimation and forecasting," Documents de Travail de l'OFCE 2005-10, Observatoire Francais des Conjonctures Economiques (OFCE).
  3. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
  4. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
  5. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representations of the Output Gap," Birkbeck Working Papers in Economics and Finance 0619, Birkbeck, Department of Economics, Mathematics & Statistics.
  6. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2008. "Forecasting with Equilibrium-correction Models during Structural Breaks," Economics Series Working Papers 408, University of Oxford, Department of Economics.
  7. Margaret M. McConnell & Gabriel Perez Quiros, 1997. "Output fluctuations in the United States: what has changed since the early 1980s?," Research Paper 9735, Federal Reserve Bank of New York.
  8. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  9. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  10. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
  11. Kishor, N. Kundan & Koenig, Evan F., 2005. "VAR estimation and forecasting when data are subject to revision," Working Papers 0501, Federal Reserve Bank of Dallas.
  12. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
  13. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
  14. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
  15. Michael P. Clements & Ana Beatriz Galvão, 2013. "Real‐Time Forecasting Of Inflation And Output Growth With Autoregressive Models In The Presence Of Data Revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 458-477, 04.
  16. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  17. P. Geoffrey Allen & Robert Fildes, 2005. "Levels, Differences and ECMs - Principles for Improved Econometric Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 881-904, December.
  18. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
  19. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197.
  20. Koenig, Evan F. & Dolmas, Sheila & Piger, Jeremy M., 2000. "The use and abuse of "real-time" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
  21. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  22. J. Steven Landefeld & Eugene P. Seskin & Barbara M. Fraumeni, 2008. "Taking the Pulse of the Economy: Measuring GDP," Journal of Economic Perspectives, American Economic Association, vol. 22(2), pages 193-216, Spring.
  23. Herman O. Stekler & Raj M. Talwar, 2011. "Economic Forecasting in the Great Recession," Working Papers 2011-005, The George Washington University, Department of Economics, Research Program on Forecasting.
  24. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier.
  25. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2011. "Forecasting breaks and forecasting during breaks," Economics Series Working Papers 535, University of Oxford, Department of Economics.
  26. 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.
  27. Peña, Daniel & Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.
  28. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
  29. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
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