Semi-Parametric Analysis of Efficiency and Productivity using Gaussian Processes

Gregory Emvalomatis (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

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 it is shown in simulated data to perform as well as flexible 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 with both parametric and semi-parametric techniques.

Original languageEnglish
Pages (from-to)48-67
Number of pages20
JournalThe Econometrics Journal
Volume23
Issue number1
Early online date5 Sep 2019
DOIs
Publication statusPublished - Jan 2020

Keywords

  • Gaussian-process regression
  • stochastic frontier
  • TFP decomposition
  • Gaussian process regression
  • total-factor productivity decomposition

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