Reference: | Assaf, A.G., R. Matousek and E.G. Tsionas, (2013). Turkish bank efficiency: Bayesian estimation with undesirable outputs, Journal of Banking and Finance, 37, 506-517.
Atkinson, S.E. and J.H. Dorfman, (2005). Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution, Journal of Econometrics, 126, 445-468.
Battese, G.E. and T.J. Coelli, (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data, Empirical Economics, 20, 325-332.
Breheny, P., (Retrieved September 26 2016). MCMC methods: Gibbs and Metropolis, from: http://web.as.uky.edu/statistics/users/pbreheny/701/S13/notes/2-28.pdf
Bokusheva, R. and S.C. Kumbhakar, (2014). A distance function model with good and bad outputs, from: http://ageconsearch.umn.edu/bitstream/182765/2/Bokusheva-Distance_function_model_with_good_and_bad_outputs-258_a.pdf
Boyd, G.A., G. Tolley and J. Pang, (2002). Plant level productivity, efficiency, and environmental performance of the container glass industry, Environmental and Resource Economics, 23, 29-43.
Broeck, J. van den, G. Koop, J. Osiewalski and M.F.J. Steel, (1994). Stochastic frontier models: a Bayesian perspective, Journal of Econometrics, 61, 273-303.
Chambers, R.G., (2002). Exact nonradial input, output, and productivity measurement, Economic Theory, 20, 751-765.
Chung, Y.H., R. Färe and S. Grosskopf, (1997). Productivity and undesirable outputs: a directional distance function approach, Journal of Environmental Management, 51, 229-240.
Cowles, M.K. and B.P. Carlin, (Retrieved September 26 2016). Markov chain Monte Carlo convergence diagnostics: a comparative review, from: http://www.public.iastate.edu/~alicia/stat544/rr94-008.pdf
Färe, R. and S. Grosskopf, (2003). New directions: efficiency and productivity, US: Springer.
Färe, R., S. Grosskopf, D. Noh and W. Weber, (2005). Characteristics of a polluting technology, Journal of Econometrics, 126, 469-492.
Feng, G. and A. Serletis, (2010). A primal Divisia technical change index based on the output distance function, Journal of Econometrics, 159, 320-330.
Feng, G. and A. Serletis, (2014). Undesirable outputs and a primal Divisia productivity index based on the directional output distance function, Journal of Econometrics, 183, 135-146.
Feng, G. and X.H. Zhang, (2012). Productivity and efficiency at large and community banks in the US: a Bayesian true random effects stochastic distance frontier analysis, Journal of Banking and Finance, 36, 1883-1895.
Fernández, C., G.M. Koop and M. Steel, (2002). Multiple output production with undesirable outputs: an application to nitrogen surplus in agriculture, Journal of the American Statistical Association, 97, 432-442.
Fernández, C., J. Osiewalski and M.F.J. Steel, (1997). On the use of panel data in stochastic frontier models with improper priors, Journal of Econometrics, 79,169-193.
Flegal, J.M., (2008). Monte Carlo standard errors for Markov chain Monte Carlo, from: http://www.faculty.ucr.edu/~jflegal/Final_Thesis_twosided.pdf
Griffin, J.E. and M.F.J. Steel, (2007). Bayesian stochastic frontier analysis using WinBUGS, Journal of Productivity Analysis, 27, 163-176.
Griffin, J. E. and M.F.J. Steel, (2008). Flexible mixture modeling of stochastic frontiers, Journal of Productivity Analysis, 29, 33-50.
Griffiths, W.E., C.J. O’Donnell and A.T. Cruz, (2000). Imposing regularity conditions on a system of cost and factor share equations, The Australian Journal of Agricultural and Resource Economics, 44, 107-127.
Huang, C.J. and J.-T. Liu, (1994). Estimation of a non-neutral stochastic frontier production function, Journal of Productivity Analysis, 5, 171-180.
Huang, H.C., (2004). Estimation of technical inefficiencies with heterogeneous technologies, Journal of Productivity Analysis, 21, 277-296.
Huang, T.-H., (2005). A study on the productivities of IT capital and computer labor: firm-level evidence from Taiwan’s banking industry, Journal of Productivity Analysis, 24, 241-257.
Kleit, A.N. and D. Terrell, (2001). Measuring potential efficiency gains from deregulation electricity generation: a Bayesian approach, Review of Economics and Statistics, 83, 523-530.
Koop, G., J. Osiewalski and M.F.J. Steel, (1994b). Hospital efficiency analysis with individual effects: a Bayesian approach, Center for Economic Research discussion paper, 9447.
Koop, G., J. Osiewalski and M.F.J. Steel, (1997). Bayesian efficiency analysis through individual effects: hospital cost frontiers, Journal of Econometrics, 76, 77-105.
Kurkalova, L. A. and A. Carriquiry, (2003). Input- and output-oriented technical efficiency of Ukrainian collective farms, 1989-1992: Bayesian analysis of a stochastic production frontier model, Journal of Productivity Analysis, 20, 191-211.
Lam, P., (Retrieved September 26 2016). MCMC methods: Gibbs sampling and the Metropolis-Hastings algorithm, from: http://pareto.uab.es/mcreel/IDEA2015/MCMC/mcmc.pdf
Lee, J.-D., J.-B. Park and T.-Y. Kim, (2002). Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach, Journal of Environmental Management, 63, 365-375.
Lin, E.T.J. and L.W. Lan, (2010). Measuring firm-specific efficiencies with Bayesian stochastic distance function, 2010 International Conference on Asia Pacific Business Innovation and Technology Management.
Morey, E.R., (1986). An introduction to checking, testing, and imposing curvature properties: the true function and the estimated function, Canadian Journal of Economics, 19, 207-235.
O’Donnell, C.J. and T.J. Coelli, (2005). A Bayesian approach to imposing curvature on distance functions, Journal of Econometrics, 126, 493-523.
Orea, L., (2002). Parametric decomposition of a generalized Malmquist productivity index, Journal of Productivity Analysis, 18, 5-22.
Osiewalski, J. and M.F.J. Steel, (1998). Numerical tools for the Bayesian analysis of stochastic frontier models, Journal of Productivity Analysis, 10, 103-117.
Sinharay, S., (2003). Assessing convergence of the Marlov chain Monte Carlo algorithms: a review, from: http://www.ets.org/Media/Research/pdf/RR-03-07-Sinharay.pdf
Terrell, D., (1996). Incorporating monotonicity and concavity conditions in flexible functional forms, Journal of Applied Econometrics, 11, 179-194.
Zhang, X., (1999). A Monte Carlo study on the finite sample properties of the Gibbs sampling method for a stochastic frontier model, Journal of Productivity Analysis, 14, 71-83. |