Identification and nonparametric estimation of a transformed additively separable model

Jacho-Chávez, David, Lewbel, Arthur and Linton, Oliver (2006) Identification and nonparametric estimation of a transformed additively separable model. . Suntory and Toyota International Centres for Economics and Related Disciplines, London, UK.

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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.

Item Type:	Monograph (Discussion Paper)
Official URL:	http://sticerd.lse.ac.uk
Additional Information:	© 2006 the authors
Divisions:	Financial Markets Group Economics STICERD
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods D - Microeconomics > D2 - Production and Organizations > D24 - Production; Cost; Capital and Total Factor Productivity; Capacity C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models
Date Deposited:	21 Apr 2008 10:36
Last Modified:	13 Sep 2024 20:01
URI:	http://eprints.lse.ac.uk/id/eprint/4416

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