Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096966 | Journal of Econometrics | 2010 | 16 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
David Jacho-Chávez, Arthur Lewbel, Oliver Linton,