IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v28y2001i6p775-778.html
   My bibliography  Save this article

Remarks on a 'critique' of the Bayesian Method of Moments

Author

Listed:
  • Arnold Zellner

Abstract

No abstract is available for this item.

Suggested Citation

  • Arnold Zellner, 2001. "Remarks on a 'critique' of the Bayesian Method of Moments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 775-778.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:775-778
    DOI: 10.1080/02664760120059291
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059291
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760120059291?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zellner, Arnold, 1988. "Bayesian analysis in econometrics," Journal of Econometrics, Elsevier, vol. 37(1), pages 27-50, January.
    2. Jeffrey T. LaFrance, 1999. "Inferring the Nutrient Content of Food With Prior Information," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 728-734.
    3. Zellner, Arnold, 1998. "The finite sample properties of simultaneous equations' estimates and estimators Bayesian and non-Bayesian approaches," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 185-212.
    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. Zellner, Arnold, 2006. "S. James Press And Bayesian Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 10(5), pages 667-684, November.
    2. Zellner, Arnold, 2007. "Some aspects of the history of Bayesian information processing," Journal of Econometrics, Elsevier, vol. 138(2), pages 388-404, June.

    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. Zellner, Arnold, 2007. "Some aspects of the history of Bayesian information processing," Journal of Econometrics, Elsevier, vol. 138(2), pages 388-404, June.
    2. Scott E. Atkinson & Jeffrey H. Dorfman, 2009. "Feasible estimation of firm-specific allocative inefficiency through Bayesian numerical methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 675-697.
    3. Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
    4. Heckelei, Thomas & Mittelhammer, Ron C., 2003. "Bayesian bootstrap multivariate regression," Journal of Econometrics, Elsevier, vol. 112(2), pages 241-264, February.
    5. Chuanming Gao & Kajal Lahiri, 2019. "A Comparison of Some Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments," Econometrics, MDPI, vol. 7(3), pages 1-28, July.
    6. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    7. Radchenko, Stanislav & Tsurumi, Hiroki, 2006. "Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market," Journal of Econometrics, Elsevier, vol. 133(1), pages 31-49, July.
    8. Zellner, Arnold & Ando, Tomohiro, 2010. "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting," International Journal of Forecasting, Elsevier, vol. 26(2), pages 413-434, April.
    9. Zellner, Arnold, 2006. "S. James Press And Bayesian Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 10(5), pages 667-684, November.
    10. Kleibergen, Frank & Zivot, Eric, 2003. "Bayesian and classical approaches to instrumental variable regression," Journal of Econometrics, Elsevier, vol. 114(1), pages 29-72, May.
    11. Chuanming Gao & Kajal Lahiri, 2000. "A Comparison of Some Recent Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments," Econometric Society World Congress 2000 Contributed Papers 0230, Econometric Society.
    12. Zellner, Arnold, 1999. "Discussion of Papers Presented at 1999 ASSA Meeting in New York By (1) Foster and Whiteman, (2) Golan, Moretti and Perloff, and (3) LaFrance," CUDARE Working Papers 198675, University of California, Berkeley, Department of Agricultural and Resource Economics.
    13. Marcet, Albert & Jarociński, Marek, 2010. "Autoregressions in small samples, priors about observables and initial conditions," Working Paper Series 1263, European Central Bank.
    14. Donald W.K. Andrews & James H. Stock, 2005. "Inference with Weak Instruments," NBER Technical Working Papers 0313, National Bureau of Economic Research, Inc.
    15. Melvin Novick, 1980. "Statistics as psychometrics," Psychometrika, Springer;The Psychometric Society, vol. 45(4), pages 411-424, December.
    16. Georges Bresson & Cheng Hsiao & Alain Pirotte, 2011. "Assessing the contribution of R&D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 435-452, December.
    17. Steel, M.F.J., 1988. "Seemingly unrelated regression equation systems under diffuse stochastic prior information : A recursive analytical approach," Other publications TiSEM 6d8c95f6-dc7f-4fb9-ae6b-3, Tilburg University, School of Economics and Management.
    18. Diebold, Francis X. & Lamb, Russell L., 1997. "Why are estimates of agricultural supply response so variable?," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 357-373.
    19. Steel, Mark F. J., 1991. "A Bayesian analysis of simultaneous equation models by combining recursive analytical and numerical approaches," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 83-117.
    20. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.

    More about this item

    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:taf:japsta:v:28:y:2001:i:6:p:775-778. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.