Bayesian approach to disentangling technical and environmental productivity

Econometrics

Volumn 3, Issue 2, 2015, Pages 443-465

  Malikov, Emir ^a   Kumbhakar, Subal C ^b,c   Tsionas, Efthymios G ^d  

^a ST LAWRENCE UNIVERSITY   (United States)

^b BINGHAMTON UNIVERSITY   (United States)

^c NORWEGIAN INSTITUTE OF BIOECONOMY RESEARCH   (Norway)

^d LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Bad output; By production; Efficiency; MCMC; Productivity

Indexed keywords

EID: 84999064725     PISSN: None     EISSN: 22251146     Source Type: Journal    
DOI: 10.3390/econometrics3020443     Document Type: Article

References (25)

1. Atkinson, S.E.; Dorfman, J.H. Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: Crediting electric utilities for reducing air polution. J. Econ. 2005, 126, 445–468.
2. Atkinson, S.E.; Tsionas, E.G. Directional distance functions: Optimal endogenous directions. J. Econ. 2015, in press.
3. Reinhard, S.; Lovell, C.A.K.; Thijssen, G. Econometric estimation of technical and environmental efficiency: An application to Dutch dairy farms. Am. J. Agric. Econ. 1999, 81, 44–60.
4. Reinhard, S.; Lovell, C.A.K.; Thijssen, G. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res. 2000, 121, 287–303.
5. Hailu, A.; Veeman, T.S. Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. Am. J. Agric. Econ. 2001, 83, 605–616.
6. Färe, R.; Grosskopf, S.; Noh, D.W.; Weber, W. Characteristics of a polluting technology: Theory and practice. J. Econ. 2005, 126, 469–492.
7. Murty, S.; Russell, R.R.; Levkoff, S.B. On modeling pollution-generating technologies. J. Environ. Econ. Manag. 2012, 64, 117–135.
8. Chung, Y.; Färe, R.; Grosskopf, S. Productivity and undesirable outputs: A directional distance function approach. J. Environ. Manag. 1997, 51, 229–240.
9. Feng, G.; Serletis, A. Undesirable outputs and a primal Divisia productivity index based on the directional output distance function. J. Econ. 2014, 183, 135–146.
10. Färe, R.; Grosskopf, S. Directional distance functions and slacks-based measures of efficiency. Eur. J. Oper. Res. 2010, 200, 320–322.
11. Fernández, C.; Koop, G.; Steel, M.F.J. Multiple-output production with undesirable outputs: An application to Nitrogen Surplus in Agriculture. J. Am. Stat. Assoc. 2002, 97, 432–442.
12. Fernández, C.; Koop, G.; Steel, M.F.J. Alternative efficiency measures for multiple-output production. J. Econ. 2005, 126, 411–444.
13. Forsund, F. Good modelling of bad outputs: Pollution and multiple-output production. Int. Rev. Environ. Resour. Econ. 2009, 3, 1–38.
14. Solow, R.M. Technical change and the aggregate production function. Rev. Econ. Stat. 1957, 39, 312–320.
15. Barnett, W.A.; Geweke, J.; Wolfe, M. Seminonparametric Bayesian estimation of the Asymptotically Ideal Production Model. J. Econ. 1991, 49, 5–50.
16. Barnett, W.A. Tastes and technology: Curvature is not sufficient for regularity. J. Econ. 2002, 108, 199–202.
17. Pasurka, C.A. Decomposing electric power plan emissions within a joint production framework. Energy Econ. 2006, 28, 26–43.
18. O’Donnell, C.J.; Coelli, T.J. A Bayesian approach to imposing curvature on distance functions. J. Econ. 2005, 126, 493–523.
19. Koop, G.; Steel, M. Bayesian analysis of stochastic frontier models. In A Companion to Theoretical Econometrics; Baltagi, B., Ed.; Blackwell Publishing Ltd.: Malden, MA, USA, 2001.
20. Berger, J.O.; Mortera, J. Default Bayes factors for nonnested hypothesis testing. J. Am. Stat. Assoc. 1999, 94, 542–554.
21. Mulder, J.; Hoijtink, H.; Klugkist, I. Equality and inequality constrained multivariate linear models: Objective model selection using constrained posterior priors. J. Stat. Plan. Inference 2010, 140, 887–906.
22. Berger, J.O.; Pericchi, L.R. The intrinsic Bayes factor for model selection and prediction. J. Am. Stat. Assoc. 1996, 91, 109–122.
23. Geweke, J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics 4; Bernardo, J.M., Berger, J.O., Dawid, A.P., Smith, A.F.M., Eds.; Clarendon Press: Oxford, UK, 1992.
24. Tsionas, E.G.; Kumbhakar, S.C. Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random-effects model. J. Appl. Econ. 2014, 29, 110–132.
25. Verdinelli, I.; Wasserman, L. Computing Bayes factors by using a generalization of the Savage-Dickey density ratio. J. Am. Stat. Assoc. 1995, 90, 614–618.