Econometric Analysis of Productivity: Theory and Implementation in R

Robin C. Sickles (), Wonho Song () and Valentin Zelenyuk
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Robin C. Sickles: Department of Economics, Rice University
Wonho Song: School of Economics, Chung-Ang University

No WP082018, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: Our chapter details a wide variety of approaches used in estimating productivity and efficiency based on methods developed to estimate frontier production using Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). The estimators utilize panel, single cross section, and time series data sets. The R programs include such approaches to estimate firm efficiency as the time invariant fixed effects, correlated random effects, and uncorrelated random effects panel stochastic frontier estimators, time varying fixed effects, correlated random effects, and uncorrelated random effects estimators, semi-parametric efficient panel frontier estimators, factor models for cross-sectional and time-varying efficiency, bootstrapping methods to develop confidence intervals for index number-based productivity estimates and their decompositions, DEA and Free Disposable Hull estimators. The chapter provides the professional researcher, analyst, statistician, and regulator with the most up to date efficiency modelling methods in the easily accessible open source programming language R.

Keywords: Production (technical) efficiency; Stochastic frontier analysis; Data envelopment analysis; Panel data; Index numbers; Non-parametric analysis; Bootstrapping (search for similar items in EconPapers)
JEL-codes: C10 C13 C14 C15 C44 C50 D24 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-ecm and nep-eff
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Working Paper: Econometric Analysis of Productivity: Theory and Implementation in R (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:qld:uqcepa:129

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