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Faculty of Economics

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Jacho-Chavez, D., Lewbel, A. and Linton, O.

Identification and nonparametric estimation of a transformed additively separable model

Journal of Econometrics

Abstract: Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses the identification and consistent estimation of the unknown functions H, M, G and F, where r(x,z)=H[M(x,z)], M(x,z)=G(x)+F(z), and H is strictly monotonic. An estimation algorithm is proposed for each of the model’s unknown components when r(x,z) represents a conditional mean function. The resulting estimators use marginal integration to separate the components G and F. Our estimators are shown to have a limiting Normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives. Their small sample performance is studied in a Monte Carlo experiment. We apply our results to estimate generalized homothetic production functions for four industries in the Chinese economy.

JEL Codes: C13, C14, C21, D24

Author links: Oliver Linton  

Publisher's Link: http://www.sciencedirect.com/science/article/pii/S0304407609002978



Papers and Publications



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