Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-27T18:12:44.787Z Has data issue: false hasContentIssue false

A PUZZLING PHENOMENON IN SEMIPARAMETRIC ESTIMATION PROBLEMS WITH INFINITE-DIMENSIONAL NUISANCE PARAMETERS

Published online by Cambridge University Press:  18 July 2008

Kohtaro Hitomi
Affiliation:
Kyoto Institute of Technology
Yoshihiko Nishiyama
Affiliation:
Kyoto University
Ryo Okui*
Affiliation:
Hong Kong University of Science and Technology
*
Address correspondence to Ryo Okui, Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; e-mail: [email protected]

Abstract

This note considers a puzzling phenomenon that is observed in some semiparametric estimation problems. In some cases, using estimated values of the nuisance parameters provides a more efficient estimator for the parameters of interest than does using the true values. This phenomenon takes place even in cases of semi-nonparametric models in which the nuisance parameters are infinite-dimensional and cannot be estimated at the parametric rate. We examine the structure and present the necessary and sufficient condition for the occurrence of this puzzle. We also provide a simple sufficient condition. It shows that the puzzle occurs when the term accounting for the effect of estimation of nuisance parameters is included in the tangent space. This condition is often satisfied when the estimating equation does not bring any restriction on the form of the nuisance parameters. Our simple sufficient condition can be applied to many important estimators.

Type
Notes and Problems
Copyright
Copyright © Cambridge University Press 2008

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

Bickel, P.J., Klassen, C.A.J., Ritov, Y., & Wellner, J.A. (1993) Efficient and Adaptive Estimation for Semiparametric Models. Johns Hopkins University Press.Google Scholar
Chen, X., Hong, H., & Tarozzi, A. (2008) Semiparametric efficiency in GMM models with auxiliary data. Annals of Statistics 36, 808843.Google Scholar
Hahn, J. (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66, 315331.Google Scholar
Henmi, M. (2004) A paradoxical effect of nuisance parameters on efficiency of estimators. Journal of the Japan Statistical Society 34, 7586.Google Scholar
Henmi, M. & Eguchi, S. (2004) A paradox concerning nuisance parameters and projected estimating functions. Biometrika 91, 929941.CrossRefGoogle Scholar
Hirano, K., Imbens, G.W., & Ridder, G. (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 11611189.CrossRefGoogle Scholar
Imbens, G.W. (1992) An efficient method of moments estimator for discrete choice models with choice-based sampling. Econometrica 60, 11871214.CrossRefGoogle Scholar
Lawless, J.F., Kalbfleisch, J.D., & Wild, C.J. (1999) Semiparametric methods for response-selective and missing data problems in regression. Journal of the Royal Statistical Society, Series B 61, 413438.Google Scholar
Lee, M.J. (1996) Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer-Verlag.CrossRefGoogle Scholar
Lewbel, A. (2000) Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables. Journal of Econometrics 97, 145177.Google Scholar
Lewbel, A. & Schennach, S.M. (2007) A simple ordered data estimator for inverse density weighted expectations. Journal of Econometrics 136, 189211.Google Scholar
Magnac, T. & Maurin, E. (2007) Identification and information in monotone binary models. Journal of Econometrics 139, 76104.Google Scholar
Newey, W.K. (1990) Semiparametric efficiency bounds. Journal of Applied Econometrics 5, 99135.Google Scholar
Newey, W.K. & Ruud, P.A. (2005) Density weighted linear least squares. In Andrews, D.W.K. & Stock, J.H. (eds.), Identification and Inference for Econometric Models, Essays in Honor of Thomas Rothenberg, pp. 554573. Cambridge University Press.CrossRefGoogle Scholar
Pierce, D.A. (1982) The asymptotic effect of substituting estimators for parameters in certain types of statistics. Annals of Statistics 10, 475478.Google Scholar
Prokhorov, A. & Schmidt, P. (2006) GMM Redundancy Results for General Missing Data Problems. Mimeo, Concordia University, Canada.Google Scholar
Robins, J.M., Mark, S.D., & Newey, W.K. (1992) Estimating exposure effects by modeling the expectation of exposure conditional on confounders. Biometrics 48, 479495.Google Scholar
Robins, J.M. & Rotnitzky, A. (1995) Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90, 122129.Google Scholar
Robins, J.M., Rotnitzky, A., & Zhao, L.P. (1994) Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846866.Google Scholar
Rosenbaum, P.R. (1987) Model-based direct adjustment. Journal of the American Statistical Association 82, 387394.Google Scholar
Suzukawa, A. (2004) Unbiased estimation of functionals under random censorship. Journal of the Japan Statistical Society 34, 153172.Google Scholar
Wooldridge, J.M. (1999) Asymptotic properties of weighted M-estimators for variable probability samples. Econometrica 67, 13851406.Google Scholar
Wooldridge, J.M. (2001) Asymptotic properties of weighted M-estimators for standard stratified samples. Econometric Theory 17, 451470.CrossRefGoogle Scholar
Wooldridge, J.M. (2007) Inverse probability weighted estimation for general missing data problems. Journal of Econometrics 141, 12811301.Google Scholar