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A Consistent Model Specification Test for Nonparametric Estimation of Regression Function Models

Published online by Cambridge University Press:  11 February 2009

Pedro L. Gozalo
Affiliation:
Brown University

Abstract

This paper proposes a general framework for specification testing of the regression function in a nonparametric smoothing estimation context. The same analysis can be applied to cases as varied as testing for omission of variables, testing certain nonlinear restrictions in the regressors, and testing the correct specification of some parametric or semiparametric model of interest, for example, testing a certain type of nonlinearity of the regression function. Furthermore, the test can be applied to i.i.d. and time-series data, and some or all of the regressors are allowed to be discrete. A Monte Carlo simulation is used to assess the performance of the test in small and medium samples.

Type
Articles
Copyright
Copyright © Cambridge University Press 1993

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References

1.Andrews, D.W.K.Asymptotic normality of series estimators for nonparametric and semi-parametric regression models. Econometrica 59 (1991): 307345.CrossRefGoogle Scholar
2.Bierens, H.J.Uniform consistency of kernel estimators of a regression function under generalized conditions. Journal of the American Statistical Association 78 (1983): 699707.CrossRefGoogle Scholar
3.Bierens, H.J. Kernel estimators of regression functions. In Bewley, T.F. (ed.), Advances in Econometrics, Chapter 3. New York: Cambridge University Press, 1987.Google Scholar
4.Bierens, H.J.A consistent Hausman-type model specification test. Working Paper 1987.2, Free University, Amsterdam, 1987.Google Scholar
5.Bierens, H.J.ARMAX model specification testing, with an application to unemployment in the Netherlands. Journal of Econometrics 35 (1987): 161190.CrossRefGoogle Scholar
6.Deaton, A. Demand analysis. In Griliches, Z. and Intriligator, M.D. (eds.), Handbook of Econometrics, Chapter 30. Amsterdam: North-Holland, 1986.Google Scholar
7.Devroye, L.P.The uniform convergence of the Nadaraya-Watson regression function estimate. Canadian Journal of Statistics 6 (1978): 179191.CrossRefGoogle Scholar
8.Gozalo, P.L.Nonparametric analysis of Engle curves. Mimeo, Department of Economics, Brown University, 1989.Google Scholar
9.Härdle, W.Applied Nonparametric Regression. New York: Cambridge University Press, 1990.CrossRefGoogle Scholar
10.Härdle, W. & Stoker, T.M.. Investigating smooth multiple regression by the method of average derivative. Journal of the American Statistical Association 84 (1989): 986995.Google Scholar
11.Lee, B. J.A heteroskedasticity test robust to conditional mean misspecification. Econometrica 60 (1992): 159171.CrossRefGoogle Scholar
12.Lewbel, A.The rank of demand systems: Theory and nonparametric estimation. Econometrica 59 (1991): 711730.CrossRefGoogle Scholar
13.Nadaraya, E.A.On estimating regression. Theory of Probability and its Applications 9 (1964): 141142.CrossRefGoogle Scholar
14.Parzen, E.On estimation of a probability density function and mode. Annals of Mathematical Statistics 33 (1962): 10651076.CrossRefGoogle Scholar
15.Robinson, P.M.Nonparametric estimators for time series. Journal of Time Series Analysis 4 (1983): 185208.CrossRefGoogle Scholar
16.Robinson, P.M.On the consistency and finite sample properties of nonparametric kernel time series regression, autoregression and density estimators. Annals of the Institute of Statistical Mathematics 38 (1986): 539549.CrossRefGoogle Scholar
17.Robinson, P.M.Root-N-consistent semiparametric regression. Econometrica 56 (1988): 931954.CrossRefGoogle Scholar
18.Rosenblatt, M.Remarks on some non-parametric estimates of a density function. Annals of Mathematical Statistics 27 (1956): 832837.CrossRefGoogle Scholar
19.Schucany, W.R. & Sommers, J.P.. Improvement of kernel type density estimates. Journal of the American Statistical Association 72 (1977): 420423.CrossRefGoogle Scholar
20.Watson, G.S.Smooth regression analysis. Sankhya, Series A 26 (1964): 359372.Google Scholar