A Robust Approach to Estimating Production Functions: Replication of the ACF Procedure

17 Pages Posted: 18 Oct 2017 Last revised: 24 Aug 2018

See all articles by Kyoo il Kim

Kyoo il Kim

Michigan State University - Department of Economics

Yao Luo

University of Toronto - Department of Economics

Yingjun Su

Jinan University - Institute for Economic and Social Research

Date Written: August 1, 2018

Abstract

We study Ackerberg, Caves, and Frazer (2015)'s production function estimation method using Monte Carlos simulations. First, we replicate their results by using the same procedure and confirm the existence, as noted by ACF, of a spurious minimum in the estimation. In the population, or when the sample size is large enough, this "global" identification problem may not be a concern because the spurious minimum holds only at the extreme values of the capital and the labor coefficients. However, in the finite samples, their estimator can produce estimates that may not be clearly regarded as the spurious ones. In our second experiment, we modify the ACF procedure and show that using additional lagged instruments or a sequential search help to obtain robust estimates. We also provide some arguments why such modifications help in the ACF setting.

Keywords: Production Function, Control Function, Monte Carlo Simulation

JEL Classification: C14, C18, D24

Suggested Citation

Kim, Kyoo il and Luo, Yao and Su, Yingjun, A Robust Approach to Estimating Production Functions: Replication of the ACF Procedure (August 1, 2018). Available at SSRN: https://ssrn.com/abstract=3054939 or http://dx.doi.org/10.2139/ssrn.3054939

Kyoo il Kim (Contact Author)

Michigan State University - Department of Economics ( email )

East Lansing, MI 48824
United States

Yao Luo

University of Toronto - Department of Economics ( email )

150 St. George Street
Toronto, Ontario M5S3G7
Canada

Yingjun Su

Jinan University - Institute for Economic and Social Research ( email )

601 West Whampoa Road
Tianhe District
Guangzhou, 510632
China

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