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Econometric analysis of productivity: Theory and implementation in R

Authors
Sickles, Robin C.Song, WonHoZelenyuk, Valentin
Issue Date
Jan-2019
Publisher
Elsevier B.V.
Keywords
Bootstrapping; Data envelopment analysis; Index numbers; Nonparametric analysis; Panel data; Production (technical) efficiency; Stochastic frontier analysis
Citation
Handbook of Statistics, v.42, pp 267 - 297
Pages
31
Journal Title
Handbook of Statistics
Volume
42
Start Page
267
End Page
297
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26420
DOI
10.1016/bs.host.2018.11.007
ISSN
0169-7161
1875-7448
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, semiparametric 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 modeling methods in the easily accessible open source programming language R. © 2019 Elsevier B.V.
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