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Autor
Makieła Kamil (Cracow University of Economics, Poland / Wydział Zarządzania)
Tytuł
Bayesian Inference and Gibbs Sampling in Generalized True Random-Effects Models
Źródło
Central European Journal of Economic Modelling and Econometrics (CEJEME), 2017, vol. 9, nr 1, s. 69-95, rys., tab., aneks, bibliogr. 26 poz.
Słowa kluczowe
Wnioskowanie bayesowskie, Efektywność kosztowa, Analiza stochastyczna, Stochastyczny model graniczny, Metoda SFA (stochastyczna analiza graniczna)
Bayesian inference, Cost effectiveness, Stochastic analysis, Stochastic frontier model, Stochastic Frontier Analysis (SFA)
Uwagi
summ.; Klasyfikacja JEL: C11, C23, C51, D24
Abstrakt
The paper investigates Bayesian approach to estimate generalized true random-effects models (GTRE). The analysis shows that under suitably defined priors for transient and persistent inefficiency terms the posterior characteristics of such models are well approximated using simple Gibbs sampling. No model re-parameterization is required. The proposed modification not only allows us to make more reasonable (less informative) assumptions as regards prior transient and persistent inefficiency distribution but also appears to be more reliable in handling especially noisy datasets. Empirical application furthers the research into stochastic frontier analysis using GTRE models by examining the relationship between inefficiency terms in GTRE, true random-effects, generalized stochastic frontier and a standard stochastic frontier model. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
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Bibliografia
Pokaż
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  5. Colombi R., Martini G., Vittadini G. (2011) A Stochastic Frontier Model with short-run and long-run inefficiency random effects, No 1101,Working Papers, Department of Economics and Technology Management, University of Bergamo.
  6. Colombi R., Kumbhakar S.C., Martini G., Vittadini G. (2014) Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency. Journal of Productivity Analysis 42(2): 123-136.
  7. Feng G., Serletis A. (2009) Efficiency and productivity of the US banking industry, 1998-2005: Evidence from the Fourier cost function satisfying global regularity conditions. Journal of Applied Econometrics 24(1): 105-138. DOI:10.1002/jae.1021
  8. Fernandez C., Osiewalski J., Steel M.F.J. (1997) On the use of panel data in stochastic frontier models. Journal of Econometrics 79(1): 169-193.
  9. Filippini M., Greene W. (2016) Persistent and transient productive inefficiency: a maximum simulated likelihood approach. Journal of Productivity Analysis (45)2:187-196. DOI: 10.1007/s11123-015-0446-y
  10. Greene W. (2005) Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis 23(1): 7-32.
  11. Greene W. (2008) The Econometric Approach to Efficiency Analysis. In: H.O. Fried, C.A. Lovell, & S.S. Schmidt (ed)The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press, New York, pp.92-159.
  12. Koop G., Osiewalski J., Steel M.F.J. (1999) The Components of Output Growth: A Stochastic Frontier Analysis. Oxford Bulleting of Economics and Statistics 61(4): 455-487. DOI: 10.1111/1468-0084.00139
  13. Koop G., Osiewalski J., Steel M.F.J. (2000a) A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies. Economics of Planning 33(3): 185-202. DOI: 10.1023/A:1003919013358
  14. Koop G., Osiewalski J., Steel M.F.J. (2000b) Modeling the Sources of Output Growth in a Panel of Countries. Journal of Business & Economic Statistics18(3): 284-299. DOI: 10.2307/1392262
  15. Koop G., Steel M.F.J., Osiewalski J. (1995) Posterior analysis of stochastic frontier models using Gibbs sampling. Computational Statistics 10(10): 353-373.
  16. Kumbhakar S.C., Lien G., Hardaker J. (2014) Technical efficiency in competing panel data models: a study of Norwegian grain farming, Journal of Productivity Analysis 41(2): 321-337.
  17. Lenk P. (2009) Simulation Pseudo-Bias Correction to the Harmonic Mean Estimator of Integrated Likelihoods. Journal of Computational and Graphical Statistics 18(4): 941-960. DOI: 10.1198/jcgs.2009.08022
  18. Makieła K. (2014) Bayesian Stochastic Frontier Analysis of Economic Growth and Productivity Change in the EU, USA, Japan and Switzerland. Central European Journal of Economic Modelling and Econometrics 6(3): 193-216.
  19. Makieła K. (2009) Economic Growth Decomposition. An Empirical Analysis Using Bayesian Frontier Approach. Central European Journal of Economic Modelling and Econometrics 1(4): 333-369.
  20. Meeusen W., van den Broeck J. (1977) Efficiency estimation from Cobb-Douglas Production Function with Composed Error. International Economic Review 18(2): 435-444.
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  22. Pajor, A. (2016). Estimating the marginal likelihood using the arithmetic meanidentity. Bayesian Analysis. DOI: 10.1214/16-BA1001.
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  25. Tsionas M., Kumbhakar S.C. (2014) Firm Heterogeneity, Persistent And Transient Technical Inefficiency: A Generalized True Random-Effects model. Journal of Applied Econometrics 29(1): 110-132. DOI:10.1002/jae.2300
  26. Yu B., Mykland P. (1998) Looking at Markov samplers through cusum pathplots: a simple diagnostic idea. Statistical Computing 8(3): 275-286.
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ISSN
2080-0886
Język
eng
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