IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v78y1997i2p139-151.html
   My bibliography  Save this article

Comparing and choosing between two models with a third model in the background

Author

Listed:
  • Poirier, Dale J.

Abstract

No abstract is available for this item.

Suggested Citation

  • Poirier, Dale J., 1997. "Comparing and choosing between two models with a third model in the background," Journal of Econometrics, Elsevier, vol. 78(2), pages 139-151, June.
    • Handle: RePEc:eee:econom:v:78:y:1997:i:2:p:139-151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(96)00002-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Palm, F. & Zellner, A., 1991. "To combine or not to combine? issues of combining forecasts," LIDAM Discussion Papers CORE 1991022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Florens, J.P. & Mouchart, M., 1992. "Bayesian Testing and Testing Bayesians," Papers 92.275, Toulouse - GREMAQ.
    3. FLORENS, Jean-Pierre & MOUCHART, Michel, 1993. "Bayesian testing and testing Bayesians," LIDAM Reprints CORE 1065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, December.
    5. Poirier, Dale, 1994. "Jeffreys' prior for logit models," Journal of Econometrics, Elsevier, vol. 63(2), pages 327-339, August.
    6. Koop, Gary & Poirier, Dale J., 1993. "Bayesian analysis of logit models using natural conjugate priors," Journal of Econometrics, Elsevier, vol. 56(3), pages 323-340, April.
    7. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    8. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November.
    9. Koop, G & Poirier, D J, 1994. "Rank-Ordered Logit Models: An Empirical Analysis of Ontario Voter Preferences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(4), pages 369-388, Oct.-Dec..
    10. Poirier, Dale J. & Klepper, Steven, 1981. "Model occurrence and model selection in panel data sets," Journal of Econometrics, Elsevier, vol. 17(3), pages 333-350, December.
    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. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    2. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "Testing and Estimating Persistence in Canadian Unemployment," Econometrics 0311004, University Library of Munich, Germany.
    3. Ossama Mikhail & Curtis Eberwein & Jagdish Handa, 2005. "On the evidence of non-linear structure in Canadian unemployment," Applied Economics Letters, Taylor & Francis Journals, vol. 12(2), pages 101-104.
    4. O. Mikhail & C. J. Eberwein & J. Handa, 2006. "Estimating persistence in Canadian unemployment: evidence from a Bayesian ARFIMA," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1809-1819.

    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. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November.
    2. Rodney W. Strachan & Herman K. van Dijk, 2014. "Divergent Priors and Well Behaved Bayes Factors," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(1), pages 1-31, March.
    3. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    4. Gary Koop, 2004. "Modelling the evolution of distributions: an application to Major League baseball," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 639-655, November.
    5. Zellner, Arnold & Tobias, Justin & Ryu, Hang, 1998. "Bayesian Method of Moments (BMOM) Analysis of Parametric and Semiparametric Regression Models," CUDARE Working Papers 198660, University of California, Berkeley, Department of Agricultural and Resource Economics.
    6. Lahiri, Kajal & Gao, Jian, 2002. "Bayesian analysis of nested logit model by Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 111(1), pages 103-133, November.
    7. Gary Koop & Simon M. Potter & Rodney W. Strachan, 2008. "Re-Examining the Consumption-Wealth Relationship: The Role of Model Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 341-367, March.
    8. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    9. Gary Koop & Simon Potter, 2003. "Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging," Discussion Papers in Economics 04/16, Division of Economics, School of Business, University of Leicester.
    10. de Pooter, M.D. & Ravazzolo, F. & Segers, R. & van Dijk, H.K., 2008. "Bayesian near-boundary analysis in basic macroeconomic time series models," Econometric Institute Research Papers EI 2008-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Rodney Strachan & Herman K. van Dijk, "undated". "Bayesian Model Averaging in Vector Autoregressive Processes with an Investigation of Stability of the US Great Ratios and Risk of a Liquidity Trap in the USA, UK and Japan," MRG Discussion Paper Series 1407, School of Economics, University of Queensland, Australia.
    12. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2018. "DSGE-based Priors for BVARs & Quasi-Bayesian DSGE Estimation," Cardiff Economics Working Papers E2018/5, Cardiff University, Cardiff Business School, Economics Section.
    13. Koop, Gary & Dijk, Herman K. Van, 2000. "Testing for integration using evolving trend and seasonals models: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 97(2), pages 261-291, August.
    14. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    15. Ricardo A. Daziano & Luis Miranda-Moreno & Shahram Heydari, 2013. "Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice," Transport Reviews, Taylor & Francis Journals, vol. 33(5), pages 570-592, September.
    16. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    17. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2020. "DSGE-based priors for BVARs and quasi-Bayesian DSGE estimation," Econometrics and Statistics, Elsevier, vol. 16(C), pages 1-27.
    18. Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, vol. 88(2), pages 251-281, November.
    19. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    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:eee:econom:v:78:y:1997:i:2:p:139-151. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.