Production Function Estimation in R: The prodest Package

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Authors Gabriele Rovigatti
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract The Total Factor Productivity (TFP) also called Multi-factor productivity measures the change in output that cannot be accounted for by changes in the amounts of input. The R package prodest provides functions for TFP estimation following the most widelyknown methodologies using the control function approach. Focusing on Value Added production functions, it estimates the two–steps models presented by Olley–Pakes (1996) (Olley and Pakes 1996) and Levinshon–Petrin (2003) (Levinsohn and Petrin 2003), as well as their correction proposed by Ackerberg–Caves–Frazer (2015) (Ackerberg, Caves, and Frazer 2015). The system GMM framework proposed by Wooldridge (2009) (Wooldridge 2009) is also implemented in two slightly different versions. Dealing with standard CobbDouglas technology in a panel framework, all methods assume that the productivity term evolves according to a first-order Markov process and that a proxy variable exists i.e., a function of state variables and productivity invertible and monotonically increasing in productivity. Exploiting these features and with different choices of the proxy variables, the methods yield consistent estimates of labor and capital inputs parameters, allowing for an immediate computation of TFP. The prodest package features also the Data Generating Process used by Ackerberg–Caves–Frazer (2015) (Ackerberg, Caves, and Frazer 2015) and allows for the simulation of datasets according to several measurement errors and random shock variances. It can be used by practitioners for both running Monte Carlo simulations and benchmarking estimate results.
Date of publication 2017
Code Programming Language R

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