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Identification and nonparametric estimation of a transformed additively separable model

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

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Let r (x, z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r (x, z) = H [M (x, z)] and M (x, z) = G(x) + F (z). 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, and 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 empirically apply our results to nonparametrically estimate and test generalized homothetic production functions in four industries within the Chinese economy.

Keywords: Partly separable models; Nonparametric regression; Dimension reduction; Generalized homothetic function; Production function. (search for similar items in EconPapers)
JEL-codes: C14 D24 C13 C21 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2006-09
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http://eprints.lse.ac.uk/4416/ Open access version. (application/pdf)

Related works:
Journal Article: Identification and nonparametric estimation of a transformed additively separable model (2010) Downloads
Working Paper: Identification and Nonparametric Estimation of a Transformed Additively Separable Model (2008) Downloads
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