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<p>In this paper, we introduce a class of Generalized Autoregressive Score (GAS) panel stochastic frontier and distance function models with and without the endogeneity of the regressors, and whose coefficients are allowed to be firm-specific and vary with time. We also propose a novel class of Stochastic GAS (SGAS) panel stochastic frontier models which generalize GAS models by allowing for an additional error term in the GAS equation. Bayesian inference organized around Markov Chain Monte Carlo (MCMC) is used to estimate the parameters of the proposed models as well as inefficiency predictions. An empirical application of the Norwegian electricity sector is presented to illustrate the usefulness of our proposed models and methods.</p>

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A class of generalized autoregressive score panel stochastic frontier models

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Kien C. Tran,
  • Panayotis G. Michaelides

摘要

In this paper, we introduce a class of Generalized Autoregressive Score (GAS) panel stochastic frontier and distance function models with and without the endogeneity of the regressors, and whose coefficients are allowed to be firm-specific and vary with time. We also propose a novel class of Stochastic GAS (SGAS) panel stochastic frontier models which generalize GAS models by allowing for an additional error term in the GAS equation. Bayesian inference organized around Markov Chain Monte Carlo (MCMC) is used to estimate the parameters of the proposed models as well as inefficiency predictions. An empirical application of the Norwegian electricity sector is presented to illustrate the usefulness of our proposed models and methods.