Jacho-Chávez, David, Lewbel, Arthur and Linton, Oliver (2010) Identification and nonparametric estimation of a transformed additively separable model. Journal of Econometrics, 156 (2). pp. 392-407. ISSN 0304-4076
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.elsevier.com/wps/find/journaldescriptio... |
| Additional Information: | © 2010 Elsevier B.V. |
| Divisions: | Financial Markets Group STICERD Economics |
| 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: | 23 Jul 2010 10:55 |
| Last Modified: | 11 Dec 2024 23:40 |
| URI: | http://eprints.lse.ac.uk/id/eprint/28711 |
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