Bayesian inference and Gibbs sampling in generalized true random-effects model

Makieła, Kamil (2016): Bayesian inference and Gibbs sampling in generalized true random-effects model.

Abstract

The paper investigates Bayesian approach to estimating generalized true random-effects model (GTRE). Results show that under suitably defined priors for transient and persistent inefficiency terms the posterior characteristics of the model are well approximated using simple Gibbs sampling. Model reparametrization is unnecessary and it leads to much more time-consuming simulations. The new model also allows us to make more reasonable assumptions about prior efficiency distribution and appears more reliable in handling noisy datasets. Empirical application furthers the research into stochastic frontier analysis using GTRE by examining its relationship with other models known in the literature.

• Item Type: MPRA Paper
• Language: English
• Keywords: generalized true random-effects model, stochastic frontier analysis, Bayesian inference, cost efficiency, firm heterogeneity, transient and persistent efficiency
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity

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