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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Bayesian learning in performance
T2 - Is there any?
AU - Tsionas, Mike G.
PY - 2023/11/16
Y1 - 2023/11/16
N2 - We propose and implement a Bayesian learning model for performance. The model implies a specific distribution for performance / technical inefficiency which we exploit in the context of stochastic frontier models. As the theoretical model is ambiguous with respect to what constitutes existing “experience”, we propose and implement alternative specifications. The estimation and inference techniques are based on Bayesian analysis using Markov Chain Monte Carlo methods. We apply the new techniques to a data set of large U.S. banks. Our findings indicate that there is some learning in technical inefficiency although there is limited evidence, if at all, that jumps in experience are related to productivity growth. However, this effect is distinctly pronounced for the 2007-2010 period but much less significant afterwards.
AB - We propose and implement a Bayesian learning model for performance. The model implies a specific distribution for performance / technical inefficiency which we exploit in the context of stochastic frontier models. As the theoretical model is ambiguous with respect to what constitutes existing “experience”, we propose and implement alternative specifications. The estimation and inference techniques are based on Bayesian analysis using Markov Chain Monte Carlo methods. We apply the new techniques to a data set of large U.S. banks. Our findings indicate that there is some learning in technical inefficiency although there is limited evidence, if at all, that jumps in experience are related to productivity growth. However, this effect is distinctly pronounced for the 2007-2010 period but much less significant afterwards.
KW - Performance estimation
KW - Productivity and efficiency
KW - Bayesian learning
KW - Bayesian methods
KW - Markov chain Monte Carlo
U2 - 10.1016/j.ejor.2023.04.034
DO - 10.1016/j.ejor.2023.04.034
M3 - Journal article
VL - 311
SP - 263
EP - 282
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
ER -