IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/3677.html
   My bibliography  Save this paper

Posterior analysis of stochastic frontier models using Gibbs sampling

Author

Listed:
  • Koop, Gary
  • Steel, Mark F.J.
  • Osiewalski, Jacek

Abstract

In this paper we describe the use of Gibbs sampling methods for making posterior inferences in stochastic frontier models with composed error. We show how the Gibbs sampler can greatly reduce the computational difficulties involved in analyzing such models. Our fidings are illustrated in an empirical example.

Suggested Citation

  • Koop, Gary & Steel, Mark F.J. & Osiewalski, Jacek, 1992. "Posterior analysis of stochastic frontier models using Gibbs sampling," DES - Working Papers. Statistics and Econometrics. WS 3677, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:3677
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/3677/ws924531.pdf?sequence=1
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ley, Eduardo & Steel, Mark F J, 1996. "On the Estimation of Demand Systems through Consumption Efficiency," The Review of Economics and Statistics, MIT Press, vol. 78(3), pages 539-543, August.
    2. Supawat Rungsuriyawiboon & Chris O'Donnell, 2004. "Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities," CEPA Working Papers Series WP072004, School of Economics, University of Queensland, Australia.
    3. Danielle Lewis & Randy Anderson, 1999. "Residential Real Estate Brokerage Efficiency and the Implications of Franchising: A Bayesian Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(3), pages 543-560, September.
    4. Koop G., 2002. "Comparing the Performance of Baseball Players: A Multiple-Output Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 710-720, September.
    5. Areal, Francisco J. & Tiffin, Richard & Balcombe, Kelvin G., 2012. "Provision of environmental output within a multi-output distance function approach," Ecological Economics, Elsevier, vol. 78(C), pages 47-54.
    6. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    7. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    8. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Hospital efficiency analysis through individual effects : A Bayesian approach," Discussion Paper 1994-47, Tilburg University, Center for Economic Research.
    9. Griffiths, William E. & O'Donnell, Christopher J., 2005. "Estimating variable returns to scale production frontiers with alternative stochastic assumptions," Journal of Econometrics, Elsevier, vol. 126(2), pages 385-409, June.
    10. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    11. Seongho Song & David Yi, 2011. "The fundraising efficiency in U.S. non-profit art organizations: an application of a Bayesian estimation approach using the stochastic frontier production model," Journal of Productivity Analysis, Springer, vol. 35(2), pages 171-180, April.
    12. Philip M. Bodman, 1999. "Labour Market Inefficiency and Frictional Unemployment in Australia and its States: A Stochastic Frontier Approach," The Economic Record, The Economic Society of Australia, vol. 75(2), pages 138-148, June.
    13. Lambarraa, Fatima, 2011. "Dynamic Efficiency Analysis of Spanish Outdoor and Greenhouse Horticulture Sector," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114408, European Association of Agricultural Economists.
    14. Kamil Makieła, 2009. "Economic Growth Decomposition. An Empirical Analysis Using Bayesian Frontier Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 1(4), pages 333-369, December.
    15. Goto, Mika & Makhija, Anil K., 2007. "The Impact of Competition and Corporate Structure on Productive Efficiency: The Case of the U.S. Electric Utility Industry, 1990-2004," Working Paper Series 2007-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    2. Fernandez-Cornejo, Jorge & Wechsler, Seth James, 2012. "Fifteen Years Later: Examining the Adoption of Bt Corn Varieties by U.S. Farmers," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124257, Agricultural and Applied Economics Association.
    3. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    4. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    5. Cranfield, John A.L. & Preckel, Paul V. & Liu, Songquan, 1997. "Approximating Bayesian Posteriors using Multivariate Gaussian Quadrature," 1997 Annual Meeting, July 13-16, 1997, Reno\ Sparks, Nevada 35791, Western Agricultural Economics Association.
    6. Troske, Kenneth R. & Voicu, Alexandru, 2010. "Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques," Labour Economics, Elsevier, vol. 17(1), pages 150-169, January.
    7. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    8. Mengheng Li & Ivan Mendieta‐Muñoz, 2020. "Are long‐run output growth rates falling?," Metroeconomica, Wiley Blackwell, vol. 71(1), pages 204-234, February.
    9. Arimura, Toshi H. & Darnall, Nicole & Katayama, Hajime, 2011. "Is ISO 14001 a gateway to more advanced voluntary action? The case of green supply chain management," Journal of Environmental Economics and Management, Elsevier, vol. 61(2), pages 170-182, March.
    10. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    11. Bauwens, Luc & Bos, Charles S. & van Dijk, Herman K. & van Oest, Rutger D., 2004. "Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods," Journal of Econometrics, Elsevier, vol. 123(2), pages 201-225, December.
    12. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    13. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    14. İsmail Başoğlu & Wolfgang Hörmann & Halis Sak, 2018. "Efficient simulations for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 260(1), pages 113-128, January.
    15. Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020. "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, vol. 217(2), pages 335-355.
    16. Mengheng Li & Siem Jan (S.J.) Koopman, 2018. "Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction," Tinbergen Institute Discussion Papers 18-027/III, Tinbergen Institute.
    17. Ricardo Reis & Vasco Curdia, 2009. "Correlated Disturbances and U.S. Business Cycles," 2009 Meeting Papers 129, Society for Economic Dynamics.
    18. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Inference under Sampling from Scale Mixtures of Normals," Discussion Paper 1996-02, Tilburg University, Center for Economic Research.
    19. Boneva, T. & Golin, M. & Kaufmann, K. & Rauh, C., 2022. "Beliefs About Maternal Labor Supply," Janeway Institute Working Papers 2230, Faculty of Economics, University of Cambridge.

    More about this item

    Keywords

    Composed error models;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:3677. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.