Stochastic frontier models with random coefficients

Journal of Applied Econometrics

Volumn 17, Issue 2, 2002, Pages 127-147

  Tsionas, Efthymios G ^a  

^a ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0036255077     PISSN: 08837252     EISSN: None     Source Type: Journal    
DOI: 10.1002/jae.637     Document Type: Article

References (32)

1. Aigner D, Knox-Lovell KA, Schmidt P. 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6: 21-37.
2. Akhavein JD, Swamy PAVB, Taubman SB, Singamsetti RN. 1997. A general method of deriving the inefficiencies from a profit function. Journal of Productivity Analysis 8: 71-93.
3. Battese GE, Coelli TJ. 1988. Prediction of firm-level technical efficiencies with a generalised frontier production function and panel data. Journal of Econometrics 38: 387-399.
4. Bauer PW. 1990. Recent developments in the econometric estimation of frontiers. Journal of Econometrics 46: 39-56.
5. Casella G, George E. 1992. Explaining the Gibbs sampler. The American Statistician 46: 167-174.
6. Caudill SB, Ford JM. 1993. Biases in frontier estimation due to heteroscedasticity. Economics Letters 41: 17-20.
7. Caudill SB, Ford JM, Gropper DM. 1995. Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity. Journal of Business and Economic Statistics 13: 105-111.
8. Chib S. 1995. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90: 1313-1321.
9. Christensen LR, Greene WH. 1976. Economies of scale in U.S. electric power generation. Journal of Political Economy 84: 655-676.
10. Fernandez C, Osiewalski J, Steel MFJ. 1997. On the use of panel data in stochastic frontier models. Journal of Econometrics 79: 169-193.
11. Gelfand AE, Smith AFM. 1990. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85: 398-409.
12. Gelman A, Rubin D. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7: 457-511.
13. Geweke J. 1996. Simulation-based Bayesian inference for economic time series. Research Department, Federal Reserve Bank of Minneapolis, Working Paper 570.
14. Greene WH. 1990. A gamma-distributed stochastic frontier model. Journal of Econometrics 46: 141-163.
15. Hildreth C, Houck JP. 1968. Some estimators for a linear model with random coefficients. Journal of the American Statistical Association 63: 584-595.
16. Kalirajan KP, Obwona MB. 1994. Frontier production function: The stochastic coefficients approach. Oxford Bulletin of Economics and Statistics 56: 87-96.
17. Kalirajan KP, Shand RT. 1999. Frontier production functions and technical efficiency measures. Journal of Economic Surveys 13: 149-172.
18. Koop G, Osiewalski J, Steel MFJ. 1997. Bayesian efficiency analysis through individuals effects: Hospital cost frontiers. Journal of Econometrics 76: 77-105.
19. Koop G, Steel MFJ. 2001. Bayesian analysis of stochastic frontier models. In A Companion to Theoretical Econometrics, Baltagi B (ed.). Blackwell: Oxford; 520-573.
20. Koop G, Steel MFJ, Osiewalski J. 1995. Posterior analysis of stochastic frontier models using Gibbs sampling. Computational Statistics 10: 353-373.
21. Meeusen W, van den Broeck J. 1977. Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 8: 435-444.
22. Reinganum JF. 1989. The timing of innovation: Research, Development and Diffusion. In Handbook of Industrial Organization, Schmalensee R, Willig RD (eds). Elsevier Science Publishers: Amsterdam.
23. Roberts GO, Smith AFM. 1994. Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms. Stochastic Processes and their Applications 49: 207-216.
24. Smith AFM, Roberts CO. 1993. Bayesian computation via the Gibbs sampler and related Markov Chain Monte Carlo methods. Journal of the Royal Statistical Society B55: 3-23.
25. Stevenson RE. 1990. Likelihood functions for generalized stochastic frontier estimation. Journal of Econometrics 13: 57-66.
26. Swamy PAVB. 1970. Efficient inference in random coefficient regression models. Econometrica 38: 311-323.
27. Swamy PAVB, Tavlas GS. 1995. Random coefficient models: Theory and applications. Journal of Economic Surveys 9: 165-196.
28. Swamy PAVB, Tavlas GS. 2001. Random coefficient models. In A Companion to Theoretical Econometrics, Baltagi B (ed.). Blackwell: Oxford; 410-428.
29. Tanner MA, Wong W-H. 1987. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 82: 528-550.
30. Tsionas EG. 2000. Full likelihood inference in normal-gamma stochastic frontier models. Journal of Productivity Analysis 13: 179-201.
31. Van den Broeck J, Koop G, Osiewalski J, Steel MFJ. 1994. Stochastic frontier models: A Bayesian perspective. Journal of Econometrics 61: 273-303.
32. Zellner A. 1971. Introduction to Bayesian Inference in Econometrics. Wiley: New York.