Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach.

In this paper, we propose simulation-based Bayesian inference procedures in a cost system that includes the cost function and the cost share equations augmented to accommodate technical and allocative inefficiency. Markov chain Monte Carlo techniques are proposed and implemented for Bayesian inferen...

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Publicado en:Journal of Econometrics Vol. 126; no. 2; pp. 355 - 385
Autores principales: Kumbhakar, Subal C., Tsionas, Efthymios G.
Formato: Artículo
Publicado: Elsevier Science June 2005
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: June 2005
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        10.1016/j.jeconom.2004.05.006
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        atl: Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach.
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          Kumbhakar, Subal C.
          Tsionas, Efthymios G.
      su:
        Mathematical models
        Industrial efficiency
        Bayesian analysis
        Mathematical models of economics
        Cost
        Resource allocation -- Mathematical models
      sug:
        subj:
          Mathematical models
          Industrial efficiency
          Bayesian analysis
          Mathematical models of economics
          Cost
          Resource allocation -- Mathematical models
      ab: In this paper, we propose simulation-based Bayesian inference procedures in a cost system that includes the cost function and the cost share equations augmented to accommodate technical and allocative inefficiency. Markov chain Monte Carlo techniques are proposed and implemented for Bayesian inferences on costs of technical and allocative inefficiency, input price distortions and over- (under-) use of inputs. We show how to estimate a well-specified translog system (in which the error terms in the cost and cost share equations are internally consistent) in a random effects framework. The new methods are illustrated using panel data on U.S. commercial banks. Copyright (c) 2004 Elsevier B.V.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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