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On the Use of Artificial Regressions in Certain Microeconometric Models

Published online by Cambridge University Press:  11 February 2009

Abstract

Conditional moment tests check to see whether or not population moment equalities, implied by the null model specification, hold approximately in the sample. Asymptotically valid conditional statistics can easily be calculated from the output of a so-called outer product of the gradient (OPG) artificial regression. However, several studies have now found that this OPG variant exhibits extremely poor finite sample behavior and that significant improvements can be made by employing the efficient variant. In the light of such evidence, this paper develops new artificial regressions that can be used to calculate the efficient variant of the test statistic. These artificial regressions can also serve several other purposes, including the construction of Hausmantype tests of parameter estimator consistency.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

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References

REFERENCES

Barrow, D.F. & Cohen, A.C. (1954) On some functions involving Mill's ratio. Annals of Mathematical Statistics 25, 405–407.CrossRefGoogle Scholar
Blundell, R.. Ham, J. & Meghir, C. (1987) Unemployment and female labour supply. The Economic Journal 97, 44–64.CrossRefGoogle Scholar
Chesher, A.D. (1983) The information matrix test: A simplified calculation via a score test interpretation. Economics Letters 13, 45–48.CrossRefGoogle Scholar
Chesher, A.D. (1984) Testing for neglected heterogeneity. Econometrica 52, 865–872.CrossRefGoogle Scholar
Chesher, A.D. & Spady, R. (1991) Asymptotic expansions of the information matrix test. Econometrica 59, 787–815.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1984a) Convenient specification tests for logit and probit models. Journal of Econometrics 25, 241–262.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1984b) Model specification tests based on artificial linear regressions. International Economic Review 25, 485–502.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1987) Testing for Consistency Using Artificial Regressions. Mimeo, Department of Economics, Queen's University.Google Scholar
Davidson, R. & MacKinnon, J.G. (1988) Double-length artificial regressions. Oxford Bulletin of Economics and Statistics 50, 203–217.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1989) Testing for consistency using artificial regressions. Econometric Theory 5, 363–384.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1990) Specification tests based on artificial regressions. Journal of the American Statistical Association 85, 220–227.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1992) A new form of the information matrix test. Econometrica 60, 145–158.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1993) Estimation and Inference in Econometrics. New York: Oxford University Press.Google Scholar
Godfrey, L.G. & Wickens, M.R. (1982) Tests of misspecification using locally equivalent alternatives. In Chow, G.C. & Corsi, P. (eds.), Evaluating the Reliability of Macro-Economic Models, pp. 71–99, New York: Wiley.Google Scholar
Greene, W.H. (1992) LIMDEP, version 6.0. New York: Econometric Software.Google Scholar
Holly, A. (1982) A remark on Hausman's specification test. Econometrica 50, 749–759.CrossRefGoogle Scholar
Kennan, J. & Neumann, G.R. (1987) Why Does the Information Matrix Test Reject So Often? A Diagnosis with Some Monte Carlo Evidence. Mimeo, University of Iowa.Google Scholar
Lancaster, T. (1984) The covariance matrix of the information matrix test. Econometrica 52, 1051–1053.CrossRefGoogle Scholar
Machin, S.J. & Stewart, M.B. (1990) Unions and financial performance of British private sector establishments. Journal of Applied Econometrics 5, 327–350.CrossRefGoogle Scholar
Newey, W.K. (1985) Maximum likelihood specification testing and conditional moment tests. Econometrica 53, 1047–1070.CrossRefGoogle Scholar
Orme, C.D. (1989) Evaluating the performance of maximum likelihood corrections in the face of local misspecification. Bulletin of Economic Research 41, 29–44.CrossRefGoogle Scholar
Orme, C. (1990a) Double and Triple Length Regressions for the Information Matrix Test and Other Conditional Moment Tests. Mimeo, Department of Economics and Related Studies, University of York.Google Scholar
Orme, C. (1990b) The small sample performance of the information matrix test. Journal of Econometrics 46, 309–331.CrossRefGoogle Scholar
Orme, C. (1991) On the Use of Artificial Regressions in Certain Micro-Econometric Models. Working Paper in Economics and Econometrics 232, Australian National University.Google Scholar
White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrica 50, 1–25.CrossRefGoogle Scholar