Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-28T13:15:49.475Z Has data issue: false hasContentIssue false

The Encompassing Principle and Hypothesis Testing

Published online by Cambridge University Press:  11 February 2009

Maozu Lu
Affiliation:
University of Southampton
Grayham E. Mizon
Affiliation:
University of Southampton and European University Institute

Abstract

The exponentially tilted family of densities is used to discuss the relationship between the encompassing principle and the M-test or conditional moment testing principle. It is shown that the two principles are capable of generating the same test statistics and in this sense equivalent. However, there are differences in motivation and emphasis underlying the principles that are important in econometric modeling. In addition, parsimonious encompassing interpretations for test statistics, including those of Cox (1961, in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability I, pp. 105-123; 1962, Journal of the Royal Statistical Society, Series B 24, 406-424) and Atkinson (1970, Journal of the Royal Statistical Society, Series B 32, 323-344) are provided.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Atkinson, A.C. (1970) A method for discriminating between models. Journal of the Royal Statistical Society. Series B 32, 323344.Google Scholar
Barndorff-Nielsen, O.E. & Cox, D.R. (1989) Asymptotic Techniques for Use in Statistics. London: Chapman and Hall.CrossRefGoogle Scholar
Breusch, T.S. & Pagan, A.R. (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies 47, 239253.CrossRefGoogle Scholar
Burguete, J.F., Gallant, A.R. & Souza, G. (1982) On the unification of the asymptotic theory of non-linear econometric models. Econometric Reviews 1, 151212.CrossRefGoogle Scholar
Chesher, A. & Smith, R. (1994) Bartlett Corrections to Likelihood Ratio Tests. Discussion paper 94–371, University of Bristol.Google Scholar
Cox, D.R. (1961) Tests of separate families of hypotheses. In Neyman, J. (ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability I, pp. 105123. Berkeley: University of California Press.Google Scholar
Cox, D.R. (1962) Further results of tests of separate families of hypotheses. Journal of the Royal Statistical Society, Series B 24, 406424.Google Scholar
Daniels, H.E. (1954) Saddlepoint approximations in statistics. Annals of Mathematical Statistics 25, 631650.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1981) Several tests for model specification in the presence of alternative hypotheses. Econometrica 49, 781793.CrossRefGoogle Scholar
Engle, R.F. (1982) A general approach to Lagrange multiplier model diagnostics. Journal of Econometrics 20, 83104.CrossRefGoogle Scholar
Engle, R.F. (1984) Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In Griliches, Z. & Intriligator, M.D. (eds.), The Handbook of Econometrics, vol. 2, pp. 775826. Amsterdam: North-Holland.CrossRefGoogle Scholar
Godfrey, L.G. (1988) Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches. Cambridge: Cambridge University Press.Google Scholar
Gourieroux, C. & Monfort, A. (1995) Testing, encompassing and simulating dynamic econometric models. Econometric Theory 11, 195228.CrossRefGoogle Scholar
Gourieroux, C, Monfort, A. & Trognon, A. (1983) Testing nested or non-nested hypotheses. Journal of Econometrics 21, 83105.CrossRefGoogle Scholar
Gourieroux, C, Monfort, A. & Trognon, A. (1984) Pseudo maximum likelihood methods: Theory. Econometrica 52, 681700.CrossRefGoogle Scholar
Gurum, S. & Trivedi, P.K. (1993) Variable augmentation specification tests in the exponential family. Econometric Theory 9, 94113.CrossRefGoogle Scholar
Hendry, D.F. (1985) Monetary economic myth and econometric reality. Oxford Review of Economic Policy 1, 7284.CrossRefGoogle Scholar
Hendry, D.F. & Mizon, G.E. (1990) Procrustean econometrics: Or stretching and squeezing data. In Granger, C.W.J. (ed.), Modelling Economic Series. Readings in Econometric Methodology, PP- 121136. Oxford: Oxford University Press.Google Scholar
Hendry, D.F. & Richard, J.-F. (1982) On the formulation of empirical models in dynamic econometrics. Journal of Econometrics 20, 333.CrossRefGoogle Scholar
Hendry, D.F. & Richard, J.-F. (1983) The econometric analysis of economic time series. International Statistical Review 51, 111163.CrossRefGoogle Scholar
Hendry, D.F. & Richard, J.-F. (1989) Recent developments in the theory of encompassing. In Cornet, B. & Tulkens, H. (eds.). Contributions to Operations Research and Econometrics. The Twentieth Anniversary of CORE, pp. 393440. Cambridge, Massachusetts: MIT Press.Google Scholar
Lu, M. & Mizon, G.E. (1988) Sectorial Encompassing. Paper presented at the Macro Modelling Bureau Conference,University of Warwick.Google Scholar
Mizon, G.E. (1984) The encompassing approach in econometrics. In Hendry, D.F. & Wallis, K.F. (eds.), Econometrics and Quantitative Economics, pp. 135172. Oxford: Basil Blackwell.Google Scholar
Mizon, G.E. (1995) Progressive modelling of macroeconomic time series: The LSE methodology. In Hoover, K.D. (ed.), Macroeconomics: Developments, Tensions and Prospects, pp. 107169. Dordrecht: Kluwer Academic Press.Google Scholar
Mizon, G.E. & Richard, J.-F. (1986) The encompassing principle and its application to non-nested hypothesis tests. Econometrica 54, 657678.CrossRefGoogle Scholar
Newey, W. (1985) Maximum likelihood specirication testing and conditional moment tests. Econometrica 53, 10471070.CrossRefGoogle Scholar
Pagan, A.R. (1984) Model evaluation by variable addition. In Hendry, D.F. & Wallis, K.F. (eds.), Econometrics and Quantitative Economics, pp. 103133. Oxford: Basil Blackwell.Google Scholar
Pagan, A.R. (1989) Twenty years after: Econometrics, 1966–1986. In Cornet, B. & Tulkens, H. (eds.), Contributions to Operations Research and Econometrics. The Twentieth Anniversary of CORE, pp. 319383. Cambridge: MIT Press.Google Scholar
Pesaran, M.H. (1982). On the comprehensive method of testing non-nested regression models. Journal of Econometrics 18, 263274.CrossRefGoogle Scholar
Sawa, T. (1978) Information criteria for discriminating among alternative regression models. Econometrica 46, 12731292.CrossRefGoogle Scholar
Smith, R. (1992) Non-nested tests for competing models estimated by generalized method of moments. Econometrica 60, 973980.CrossRefGoogle Scholar
Spanos, A. (1986) Statistical Foundations of Econometric Modelling. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Spanos, A. (1990) The simultaneous equations model revisited: Statistical adequacy and identification. Journal of Econometrics 44, 87105.CrossRefGoogle Scholar
Tauchen, G. (1985) Diagnostic testing and evaluation of maximum likelihood models. Journal of Econometrics 30, 415444.CrossRefGoogle Scholar
White, H. (1981) Consequences and detection of misspecified non-linear regression models. Journal of the American Statistical Association 76, 419433.CrossRefGoogle Scholar
White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrica 50, 125.CrossRefGoogle Scholar
White, H. (1994) Estimation, Inference and Specification Analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar