Choosing among competing econometric forecasts: Regression-based forecast combination using model selection

Journal of Forecasting

Volumn 20, Issue 6, 2001, Pages 425-440

  Swanson, Norman R ^a   Zeng, Tian ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b Aeltus Investment Management   (United States)

Author keywords

Akaike and Schwarz Information Criteria; Artificial neural networks; ex ante forecasting; Forecast combination; Survey of Professional Forecasters; Unrevised data

Indexed keywords

EID: 0035444908     PISSN: 02776693     EISSN: None     Source Type: Journal    
DOI: 10.1002/for.784     Document Type: Article

References (35)

1. Akaike H. 1973. Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, Petrov BN, Csaki F (eds). Akademiai Kiado: Budapest ; 267–281.
2. Akaike H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control AC‐19: 716–723.
3. Bates JM, Granger CWJ. 1969. The combination of forecasts. Operations Research Quarterly 20: 451–468.
4. Chong YY, Hendry DF. 1986. Econometric evaluation of linear macro‐economic models. Review of Economic Studies 53: 671–690.
5. Clemen RT. 1985. Extraneous expert information. Journal of Forecasting 4: 329–348.
6. Clemen RT. 1989. Combining forecasts: a review and annotated bibliography. International Journal of Forecasting 5: 559–583.
7. Coulson NE, Robins RP. 1993. Forecast combination in a dynamic setting. Journal of Forecasting 12: 63–67.
8. Croushore D. 1993. Introducing: the survey of professional forecasters. Business Review, The Federal Reserve Bank of Philadelphia, November–December, 3–15.
9. Deutsch M, Granger CWJ, Teräsvirta T. 1994. The combination of forecasts using changing weights. International Journal of Forecasting 10: 47–57.
10. Diebold FX, Lopez JA. 1995. Forecast evaluation and combination. Handbook of Statistics. North‐Holland: Amsterdam (forthcoming).
11. Diebold FX, Mariano RS. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13: 253–263.
12. Diebold FX, Pauly P. 1987. Structural change and the combination of forecasts. Journal of Forecasting 6: 21–40.
13. Engle RF, Granger CWJ, Kraft DF. 1984. Combining competing forecasts of inflation using a bivariate ARCH model. Journal of Economic Dynamics and Control 8: 151–165.
14. Engle RF, Brown SJ. 1986. Model selection for forecasting. Applied Mathematics and Computation 20: 313–327.
15. Engle RF, Granger CWJ. 1987. Co‐integration and error correction: representation, estimation, and testing. Econometrica 55: 251–276.
16. Ericsson NR. 1992. Parameter constancy, mean square forecast errors, and measuring forecast performance: an exposition, extensions, and illustration. Journal of Policy Modeling 14: 465–495.
17. Fair RC, Shiller RJ. 1990. Comparing information in forecasts from econometric models. American Economic Review 80: 375–389.
18. Fomby TB, Guilkey DK. 1978. On choosing the optimal level of significance for the Durbin–Watson test and the Bayesian alternative. Journal of Econometrics 8: 203–213.
19. Granger CWJ. 1969. Investigating causal relation by econometric models and cross spectral methods. Econometrica 37: 428–438.
20. Granger CWJ. 1989. Combining forecasts: twenty years later. Journal of Forecasting 8: 167–173.
21. Granger CWJ, King ML, White H. 1995. Comments on testing economic theories and the use of model selection criteria. Journal of Econometrics 67: 173–187.
22. Granger CWJ, Ramanathan R. 1984. Improved methods of combining forecasts. Journal of Forecasting 3: 197–204.
23. Granger CWJ, Swanson NR. 1997. An introduction to stochastic unit root processes. Journal of Econometrics 80: 35–62.
24. Hallman J, Kamstra M. 1989. Combining algorithms based on robust estimation techniques and co‐integrating restrictions. Journal of Forecasting 8: 189–198.
25. Kuan C‐M, White H. 1994. Artificial neural networks: an econometric perspective. Econometric Reviews 13: 1–91.
26. Lu M, Mizon GE. 1991. Forecast encompassing and model evaluation. In Econometric Structural Change, Analysis and Forecasting 4, Westlund AH (ed.). Springer‐Verlag: Berlin.
27. Rissanen J. 1978. Modeling by shortest data description. Automatica 14: 465–471.
28. Russell TD, Adam EA. 1987. An empirical evaluation of alternative forecasting combinations. Management Science 33: 1267–1276.
29. Schmittlein DC, Kim J, Morrison DG. 1990. Combining forecasts: operational adjustments to theoretically optimal rules. Management Science 39: 1044–1056.
30. Schwarz G. 1978. Estimating the dimension of a model. The Annals of Statistics 6: 461–464.
31. Sin C‐Y, White H. 1995. Information criteria for selecting possibly misspecified parametric models. Journal of Econometrics 71: 207–225.
32. Swanson NR. 1996. Forecasting using first available versus fully revised economic time series data. Studies in Nonlinear Dynamics and Econometrics 1: 47–64.
33. Swanson NR, White H. 1997. A model selection approach to real‐time macroeconomic forecasting using linear models and artificial neural networks. Review of Economics and Statistics 79: 540–550.
34. White H. 1989. Learning in artificial neural networks: a statistical perspective. Neural Computation 1: 425–464.
35. White H. 1999. A reality check for data snooping. Econometrica (forthcoming).