A Further Look at Model Evaluation
In this chapter we compare the specification tests generally used in econometric model building with recently developed p-step Stone-Geisser prediction tests using jackknife procedures. The comparisons are based on a Monte Carlo study using different structural forms. The structural forms are partly misspecified. Three sets of simulations are presented: A variety of single equation models, a simple multi-equation macro model, and Klein’s Model I using US data. The jackknife based statistics enable a more critical evaluation of the out-of-sample performance of the estimated multi-equation models.
KeywordsPredictive Quality Chow Test Average Percent Error Jackknife Procedure Jackknife Estimate
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- Abrahamsen, Y. (1986), Jackknifed fixpoint estimators for linear and nonlinear econometric models. Paper presented at the International Conference Macromodels’86, Szczyrk, Poland.Google Scholar
- Abrahamsen, Y. and Schips, B. (1989), Specification and stability tests versus jackknifing. Some illustrative examples, pp. 37–43 in P. Hackl (ed.), Statistical Analysis and Economic Structural Change. Berlin: Springer-Verlag.Google Scholar
- Bergström, R. and Wold, H. (1983), Fixpoint estimation in theory and practice. Göttingen: Vandenhoeck & Ruprecht.Google Scholar
- Chong, Y.Y. and Hendry, D.F. (1986), Econometric evaluation of linear macro-economic models. Applied Economics Discussion Paper No. 10, Institute of Economics and Statistics, University of Oxford.Google Scholar
- Efron, B. and Gong, G. (1983), A leisurely look at the bootstrap, the jackknife, and cross validation. The American Statistician, 37, 36–48.Google Scholar
- Klein, L.R. (1950), Economic fluctuations in the United States 1921–1941 New York: John Wiley.Google Scholar
- Learner, E.E. (1983), Let’s take the con out of econometrics. American Economic Review, 33, 31–43.Google Scholar
- Miller, R.G. (1974), The jackknife: A review. Biometrika, 61, 1–15.Google Scholar
- Stone, M. (1974), Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B-36, 111–133.Google Scholar