Semi-Parametric Analysis of Efficiency and Productivity using Gaussian Processes

Gregory Emvalomatis (Lead / Corresponding author)

Research output: Contribution to journalArticle

Abstract

This paper proposes a fully Bayesian semi-parametric method for efficiency and productivity analysis based on Gaussian processes. The proposed technique frees the researcher from having to specify a functional form for the production frontier and is shown in simulated data to perform as well as exible parametric models when correct distributional assumptions are imposed on the inefficiency component of the error term and slightly better when incorrect assumptions are made. The technique is applied to a panel dataset of US electric utilities, where Total Factor Productivity growth is estimated and decomposed using both parametric and semi-parametric techniques.
Original languageEnglish
Article numberutz013
Number of pages20
JournalThe Econometrics Journal
DOIs
Publication statusPublished - 5 Sep 2019

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Productivity
Electric utilities

Keywords

  • Gaussian-process regression
  • stochastic frontier
  • TFP decomposition

Cite this

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