Battese-Coelli estimator with endogenous regressors
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Publication:608860
DOI10.1016/J.ECONLET.2010.08.008zbMath1201.62145OpenAlexW2043639950MaRDI QIDQ608860
Publication date: 26 November 2010
Published in: Economics Letters (Search for Journal in Brave)
Full work available at URL: https://scholarworks.utrgv.edu/cgi/viewcontent.cgi?article=1029&context=ef_fac
Applications of statistics in engineering and industry; control charts (62P30) Production models (90B30)
Related Items (15)
Endogeneity in stochastic frontier models ⋮ Some models for stochastic frontiers with endogeneity ⋮ Dependence modeling in stochastic frontier analysis ⋮ Endogenous environmental variables in stochastic frontier models ⋮ On the estimation of zero-inefficiency stochastic frontier models with endogenous regressors ⋮ A constrained state space approach for estimating firm efficiency ⋮ A spatial stochastic frontier model with endogenous frontier and environmental variables ⋮ On distinguishing the direct causal effect of an intervention from its efficiency-enhancing effects ⋮ Maximum likelihood estimation of stochastic frontier models with endogeneity ⋮ Estimation of market power in the presence of firm level inefficiencies ⋮ Endogeneity in stochastic frontier models: copula approach without external instruments ⋮ A time-varying true individual effects model with endogenous regressors ⋮ Estimating stochastic production frontiers: a one-stage multivariate semiparametric Bayesian concave regression method ⋮ A distribution-free stochastic frontier model with endogenous regressors ⋮ A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency
Cites Work
- Semiparametric efficient estimation of dynamic panel data models
- Estimation of market power in the presence of firm level inefficiencies
- Semiparametric-efficient estimation of AR(1) panel data models.
- Time-varying parameter models with endogenous regressors
- Dealing with endogeneity in a time‐varying parameter model: joint estimation and two‐step estimation procedures
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