Semiparametric smooth coefficient estimation of a production system

Kumbhakar, S; Sun, K; Zhang, R

Abstract

This paper addresses endogeneity of inputs in estimating a semiparametric smooth coefficient
production function using a system approach. The system consists of a translog production
function and the first-order conditions (FOC’s) of profit maximization. Each coefficient of the
production function is an unknown function of some exogenous environmental variables. This
makes the production function observation-specific so long as the environmental variables are
observation-specific. The estimation of the system involves applying the functional coefficient
instrumental variable method (Cai, Das, Xiong and Wu 2006) for the endogeneity of inputs in
the first step, and the semiparametric smooth coefficient seemingly unrelated regression method
(Henderson, Kumbhakar, Li and Parmeter 2015) in the second step. Using a Chinese food
industry data set, we show that the semiparametric system approach gives most economically
meaningful input elasticity estimates, compared with alternative models. We also calculate the
returns to scale along with the technical and allocative inefficiency estimates.

Citation

Kumbhakar, S., Sun, K., & Zhang, R. (2016). Semiparametric smooth coefficient estimation of a production system. Pacific Economic Review, 21(4), 464-482. https://doi.org/10.1111/1468-0106.12193

• Journal Article Type: Article
• Acceptance Date: Apr 25, 2016
• Online Publication Date: Oct 21, 2016
• Publication Date: Oct 1, 2016
• Deposit Date: May 25, 2016
• Publicly Available Date: Oct 21, 2018
• Journal: Pacific Economic Review
• Print ISSN: 1361-374X
• Electronic ISSN: 1468-0106
• Publisher: Wiley
• Volume: 21
• Issue: 4
• Pages: 464-482
• DOI: https://doi.org/10.1111/1468-0106.12193
• Publisher URL: http://dx.doi.org/10.1111/1468-0106.12193
• Related Public URLs: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1468-0106
• Additional Information: Funders : National Natural Science Foundation of China