Kumbhakar, S, Sun, K and Zhang, R 2016, 'Semiparametric smooth coefficient estimation of a production system' , Pacific Economic Review, 21 (4) , pp. 464-482.
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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.
|Schools:||Schools > Salford Business School > Salford Business School Research Centre|
|Journal or Publication Title:||Pacific Economic Review|
|Funders:||National Natural Science Foundation of China|
|Depositing User:||Dr Kai Sun|
|Date Deposited:||25 May 2016 13:02|
|Last Modified:||31 Oct 2016 09:05|
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