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Identification and nonparametric estimation of a transformed additively separable model

David Jacho-Chávez (), Arthur Lewbel () and Oliver Linton ()

Journal of Econometrics, 2010, vol. 156, issue 2, 392-407

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.

Keywords: Partly; separable; models; Nonparametric; regression; Dimension; reduction; Generalized; homothetic; function; Production; function (search for similar items in EconPapers)
Date: 2010
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Related works:
Working Paper: Identification and Nonparametric Estimation of a Transformed Additively Separable Model (2008) Downloads
Working Paper: Identification and nonparametric estimation of a transformed additively separable model (2006) Downloads
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