Estimating Production Functions with Robustness Against Errors in the Proxy Variables

45 Pages Posted: 10 Apr 2011 Last revised: 2 Jun 2019

See all articles by Yingyao Hu

Yingyao Hu

Johns Hopkins University - Department of Economics

Guofang Huang

Purdue University

yuya sasaki

Johns Hopkins University

Date Written: January 23, 2019

Abstract

This paper proposes a new approach to the identification and estimation of production functions. It extends the literature on the structural estimation of production functions, which dates back to the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables (e.g. the investment and the material input). The proposed generalized method of moment (GMM) estimator is flexible and straightforward to apply. The method is applied to study how rapidly firms in the Chilean food-product industry adjust their inputs in response to shocks to their productivity.

Keywords: Production Functions, Proxy Variables, Measurement Errors

JEL Classification: L1, D2

Suggested Citation

Hu, Yingyao and Huang, Guofang and sasaki, yuya, Estimating Production Functions with Robustness Against Errors in the Proxy Variables (January 23, 2019). Available at SSRN: https://ssrn.com/abstract=1805213 or http://dx.doi.org/10.2139/ssrn.1805213

Yingyao Hu

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

Guofang Huang (Contact Author)

Purdue University ( email )

West Lafayette, IN 47906
United States

Yuya Sasaki

Johns Hopkins University ( email )

Baltimore, MD 20036-1984
United States

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