Skip to main content
Article thumbnail
Location of Repository

Identification and nonparametric estimation of a transformed additively separable model

By David Jacho-Chávez, Arthur Lewbel and Oliver Linton

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

Topics: HB Economic Theory
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.jeconom.2009.11.008
OAI identifier: oai:eprints.lse.ac.uk:28711
Provided by: LSE Research Online
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.elsevier.com/wps/fi... (external link)
  • http://eprints.lse.ac.uk/28711... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.