Bayesian Quantile Stochastic Frontier Analysis with Endogeneity Correction: A Sectoral Efficiency Assessment

Efficiency measurement in production processes is crucial for understanding productivity dynamics across industries. Traditional Stochastic Frontier Analysis (SFA) primarily relies on mean regression techniques, which may fail to capture the full distribution of efficiency. To address this limitation, we propose a Bayesian Quantile Stochastic Frontier Model that integrates instrumental variables to account for endogeneity in the explanatory variables. This study employs Bayesian inference and Markov Chain Monte Carlo (MCMC) methods to estimate efficiency at different quantile levels, offering a more comprehensive view of inefficiency distribution. We apply the model to the agriculture sector, comparing its performance against conventional SFA models. The results reveal that efficiency varies significantly across quantiles, highlighting heterogeneity in production behavior. Additionally, correcting for endogeneity improves efficiency estimates, preventing biases found in standard models. The sectoral efficiency analysis indicates that ICT and Energy sectors exhibit the highest efficiency levels, while Agriculture and Services experience the greatest inefficiencies. Over time, the findings suggest a gradual improvement in efficiency post-۲۰۱۶, following economic adjustments. These findings have important policy implications, particularly in addressing sector-specific inefficiencies. Policymakers should focus on reducing inefficiencies in agriculture through better resource allocation and enhancing technology adoption. Additionally, industries benefiting from high efficiency should sustain their innovation and investment strategies. This study contributes to the literature by demonstrating how Bayesian Quantile SFA can enhance efficiency estimation while addressing endogeneity biases, making it a valuable tool for econometric modeling in production analysis.

کلیدواژه ها:

Bayesian Quantile Regression ، Stochastic Frontier Analysis (SFA) ، Endogeneity Correction ، Efficiency Estimation ، Production Inefficiency