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TESTING FOR NONNESTED CONDITIONAL MOMENT RESTRICTIONS VIA CONDITIONAL EMPIRICAL LIKELIHOOD

Published online by Cambridge University Press:  30 April 2010

Taisuke Otsu*
Affiliation:
Yale University
Yoon-Jae Whang
Affiliation:
Seoul National University
*
*Address correspondence to Taisuke Otsu, Cowles Foundation for Research in Econonomics, Yale University, New Haven CT 06520, U.S.A; e-mail address: taisuke.otsu@yale.edu.

Abstract

We propose nonnested tests for competing conditional moment restriction models using the method of conditional empirical likelihood, recently developed by Kitamura, Tripathi, and Ahn (2004) and Zhang and Gijbels (2003). To define the test statistics, we use the implied conditional probabilities from conditional empirical likelihood, which take into account the full implications of conditional moment restrictions. We propose three types of nonnested tests: the moment-encompassing, Cox-type, and efficient score-encompassing tests. We derive the asymptotic null distributions and investigate their power properties against a sequence of local alternatives and a fixed global alternative. Our tests have distinct global power properties from some of the existing tests based on finite-dimensional unconditional moment restrictions. Simulation experiments show that our tests have reasonable finite sample properties and dominate some of the existing nonnested tests in terms of size-corrected powers.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Andrews, D.W.K. (1987) Asymptotic results for generalized Wald tests. Econometric Theory 3, 348358.CrossRefGoogle Scholar
Andrews, D.W.K. (1995) Nonparametric kernel estimation for semiparametric models. Econometric Theory 11, 560596.CrossRefGoogle Scholar
Borwein, J.M. & Lewis, A.S. (1993) Partially-finite programming in L 1 and the existence of maximum entropy estimates. Siam Journal of Optimization 3, 248267.Google Scholar
Chen, Y. & Kuan, C. (2002) The pseudo-true score encompassing test for non-nested hypotheses. Journal of Econometrics 106, 271295.Google Scholar
Cox, D.R. (1961) Tests of separate families of hypotheses. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. I, pp. 105123. University of California Press.Google Scholar
Cox, D.R. (1962) Further results on tests of separate families of hypotheses. Journal of the Royal Statistical Society B 24, 406424.Google Scholar
Csiszár, I. (1975) I-divergence geometry of probability distributions and minimization problems. Annals of Probability 3, 146158.Google Scholar
Csiszár, I. (1995) Generalized projections for non-negative functions. Acta Mathematica Hungarica 68, 161185.Google Scholar
Davidson, R. & MacKinnon, J. (1981) Several tests for model specification in the presence of alternative hypothesis. Econometrica 49, 781793.Google Scholar
Dhaene, G. (1997) Encompassing: Formulation, Properties and Testing. Springer.Google Scholar
Donald, S.G., Imbens, G.W., & Newey, W.K. (2003) Empirical likelihood estimation and consistent tests with conditional moment restrictions. Journal of Econometrics 117, 5593.Google Scholar
Fisher, G. & McAleer, M. (1981) Alternative procedures and associated tests of significance for non-nested hypotheses. Journal of Econometrics 16, 103119.Google Scholar
Ghysels, E. & Hall, A. (1990) Testing nonnested Euler conditions with quadrature-based methods of approximation. Journal of Econometrics 46, 273308.Google Scholar
Godfrey, L.G. (1998) Tests of non-nested regression models: Some results on small sample behaviour and the bootstrap. Journal of Econometrics 84, 5974.Google Scholar
Gourieroux, C. & Monfort, A. (1994) Testing non-nested hypotheses. In Engle, R.F. & McFadden, D.L. (eds.), Handbook of Econometrics, vol. IV, pp. 25832637. Elsevier.Google Scholar
Gourieroux, C., Monfort, A., & Trognon, A. (1983) Testing nested or non-nested hypotheses. Journal of Econometrics 21, 83115.Google Scholar
Hansen, L.P. (1982) Large sample properties of generalized method of moments estimators. Econometrica 50, 10291054.Google Scholar
Kitamura, Y. (2001) Asymptotic optimality of empirical likelihood for testing moment restrictions. Econometrica 69, 16611672.Google Scholar
Kitamura, Y. (2003) A Likelihood-Based Approach to the Analysis of a Class of Nested and Non-Nested Models. Manuscript, University of Pennsylvania.Google Scholar
Kitamura, Y. (2006) Empirical Likelihood Methods in Econometrics: Theory and Practice. Cowles Foundation Discussion Paper No. 1569, Yale University.Google Scholar
Kitamura, Y., Tripathi, G., & Ahn, H. (2004) Empirical likelihood-based inference in conditional moment restriction models. Econometrica 72, 16671714.Google Scholar
Loh, W. (1985) A new method for testing separate families of hypotheses. Journal of the American Statistical Association 80, 362368.Google Scholar
Mizon, G. & Richard, J. (1986) The encompassing principle and its application to testing non-nested hypotheses. Econometrica 54, 657678.Google Scholar
Newey, W.K. (1990) Efficient instrumental variables estimation of nonlinear models. Econometrica 58, 809837.Google Scholar
Newey, W.K. (1994) Kernel estimation of partial means and a general variance estimator. Econometric Theory 10, 233253.CrossRefGoogle Scholar
Newey, W.K. & Smith, R.J. (2004) Higher order properties of gmm and generalized empirical likelihood estimators. Econometrica 72, 219255.Google Scholar
Owen, A.B. (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75, 237249.Google Scholar
Owen, A.B. (2001) Empirical Likelihood. Chapman and Hall.Google Scholar
Pesaran, M. & Weeks, M. (2001) Non-nested hypothesis testing: An overview. In Baltagi, B. (ed.), A Companion to Econometric Theory, Ch. 13, pp. 279309. Blackwell.Google Scholar
Powell, J.L., Stock, J.L., & Stoker, T.M. (1989) Semiparametric estimation of index coefficients. Econometrica 57, 14031430.Google Scholar
Qin, J. & Lawless, J. (1994) Empirical likelihood and general estimating equations. Annals of Statistics 22, 300325.CrossRefGoogle Scholar
Ramalho, J.J.S. & Smith, R.J. (2002) Generalized empirical likelihood non-nested tests. Journal of Econometrics 107, 99125.Google Scholar
Singleton, K.J. (1985) Testing specifications of economic agents’ intertemporal optimum problems in the presence of alternative models. Journal of Econometrics 30, 391413.Google Scholar
Smith, R.J. (1992) Non-nested tests for competing models estimated by generalized method of moments. Econometrica 60, 973980.CrossRefGoogle Scholar
Smith, R.J. (1997) Alternative semi-parametric likelihood approaches to generalized method of moments estimation. Economic Journal 107, 503519.Google Scholar
Tripathi, G. & Kitamura, Y. (2003) Testing conditional moment restrictions. Annals of Statistics 31, 20592095.CrossRefGoogle Scholar
Vuong, Q.H. (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57, 307333.Google Scholar
White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrica, 50, 126.Google Scholar
Wooldridge, J. (1990) An encompassing approach to conditional mean tests with application to testing nonnested hypotheses. Journal of Econometrics 45, 331350.Google Scholar
Zhang, J. & Gijbels, I. (2003) Sieve empirical likelihood and extensions of the generalized least squares. Scandinavian Journal of Statistics 30, 124.Google Scholar