On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: some new formulations and generalizations

DOI10.1016/j.ejor.2020.04.046zbMath1487.62150OpenAlexW3025514580MaRDI QIDQ2023958

Subal C. Kumbhakar, Mike G. Tsionas

Publication date: 3 May 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2020.04.046

zbMATH Keywords

endogeneityallocative efficiencytechnical efficiencyincidental parameters problemproductivity and competitiveness

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15) Production theory, theory of the firm (91B38)

Related Items

Minimax regret priors for efficiency estimation, Role of flexible data in evaluation productivity and cost efficiency using data envelopment analysis, Stochastic frontier models with time-varying conditional variances, Combining data envelopment analysis and stochastic frontiers via a LASSO prior

Uses Software

• Stata

Cites Work

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• Reconsidering heterogeneity in panel data estimators of the stochastic frontier model
• Estimation of technical and allocative inefficiency: a primal system approach
• On estimating efficiency effects in a stochastic frontier model
• Multivariate regression models for panel data
• Formulation and estimation of stochastic frontier production function models
• Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers
• Technical and allocative efficiency in a panel stochastic production frontier system model
• Endogeneity in stochastic frontier models
• Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error
• On the Pooling of Time Series and Cross Section Data
• Stochastic Frontier Analysis
• Split-panel Jackknife Estimation of Fixed-effect Models
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Orthogonal Parameters and Panel Data
• Bayes Factors
• Consistent Estimates Based on Partially Consistent Observations
• Random Simultaneous Equations and the Theory of Production