IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v100y2001i2p381-427.html
   My bibliography  Save this article

Benchmark priors for Bayesian model averaging

Author

Listed:
  • Fernandez, Carmen
  • Ley, Eduardo
  • Steel, Mark F. J.

Abstract

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an 'automatic' or 'benchmark' prior structure that can be used in such cases. We focus on the Normal linear regression model with uncertainty in the choice of regressors. We propose a partly noninformative prior structure related to a Natural Conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g[0j]. The consequences of different choices for g[0j] are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. In addition, we examine the finite sample implications of several choices of g[0j] in a simulation study. The use of the MC^3 algorithm of Madigan and York (1995), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a 'benchmark' prior specification in a linear regression context with model uncertainty.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
  • Handle: RePEc:eee:econom:v:100:y:2001:i:2:p:381-427
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(00)00076-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
    2. Chib, B. & Osiewalski, J. & Steel, M., 1990. "Regression Models Under Competing Covariance Matrices: A Baysian Perspective," Papers 9063, Tilburg - Center for Economic Research.
    3. repec:adr:anecst:y:1991:i:23:p:04 is not listed on IDEAS
    4. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    5. Richard, J. F. & Steel, M. F. J., 1988. "Bayesian analysis of systems of seemingly unrelated regression equations under a recursive extended natural conjugate prior density," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 7-37.
    6. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    7. Phillips, Peter C. B., 1995. "Bayesian model selection and prediction with empirical applications," Journal of Econometrics, Elsevier, vol. 69(1), pages 289-331, September.
    8. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
    9. Ehrlich, Isaac, 1975. "The Deterrent Effect of Capital Punishment: A Question of Life and Death," American Economic Review, American Economic Association, vol. 65(3), pages 397-417, June.
    10. 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.
    11. Jacek Osiewalski & Mark F. J. Steel, 1993. "Regression Models under Competing Covariance Structures: A Bayesian Perspective," Annals of Economics and Statistics, GENES, issue 32, pages 65-79.
    12. Poirier, Dale J, 1988. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics," Journal of Economic Perspectives, American Economic Association, vol. 2(1), pages 121-144, Winter.
    13. Luc Bauwens, 1991. "The 'pathologie' of the Natural Conjugate Prior Density in the Regression Model," Annals of Economics and Statistics, GENES, issue 23, pages 49-64.
    14. Cornwell, Christopher & Trumbull, William N, 1994. "Estimating the Economic Model of Crime with Panel Data," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 360-366, May.
    15. repec:adr:anecst:y:1993:i:32:p:04 is not listed on IDEAS
    16. Atkinson, A. C., 1981. "Likelihood ratios, posterior odds and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 15-20, May.
    17. Ehrlich, Isaac, 1973. "Participation in Illegitimate Activities: A Theoretical and Empirical Investigation," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 521-565, May-June.
    18. Chow, Gregory C., 1981. "A comparison of the information and posterior probability criteria for model selection," Journal of Econometrics, Elsevier, vol. 16(1), pages 21-33, May.
    Full references (including those not matched with items on IDEAS)

    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. Hope Corman & H. Naci Mocan, 1996. "A Time-Series Analysis of Crime and Drug Use in New York City," NBER Working Papers 5463, National Bureau of Economic Research, Inc.
    2. Maurice J. G. Bun & Richard Kelaher & Vasilis Sarafidis & Don Weatherburn, 2020. "Crime, deterrence and punishment revisited," Empirical Economics, Springer, vol. 59(5), pages 2303-2333, November.
    3. Javier Parra Domínguez & Isabel María García Sánchez & Luis Rodríguez Domínguez, 2015. "Relationship between police efficiency and crime rate: a worldwide approach," European Journal of Law and Economics, Springer, vol. 39(1), pages 203-223, February.
    4. Eugene Braslavskiy & Firmin Doko Tchatoka & Virginie Masson, 2019. "The Importance Of Punishment Substitutability In Criminometric Studies," Bulletin of Economic Research, Wiley Blackwell, vol. 71(3), pages 491-507, July.
    5. Ruggieri, Eric & Lawrence, Charles E., 2012. "On efficient calculations for Bayesian variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1319-1332.
    6. Libor Dušek, 2012. "Crime, Deterrence, and Democracy," German Economic Review, Verein für Socialpolitik, vol. 13(4), pages 447-469, November.
    7. Kelaher, Richard & Sarafidis, Vasilis, 2011. "Crime and Punishment Revisited," MPRA Paper 28213, University Library of Munich, Germany.
    8. Chihiro Muroi & Robert Baumann, 2009. "The Non-Linear Effect of Wealth on Crime," Working Papers 0907, College of the Holy Cross, Department of Economics.
    9. Entorf, Horst & Spengler, Hannes, 2008. "Is Being 'Soft on Crime' the Solution to Rising Crime Rates? Evidence from Germany," IZA Discussion Papers 3710, Institute of Labor Economics (IZA).
    10. Alejandro Gaviria & Carlos Medina & Jorge Tamayo, 2010. "Assessing the Link between Adolescent Fertility and Urban Crime," Borradores de Economia 6860, Banco de la Republica.
    11. William S. Reece, 2010. "Casinos, Hotels, And Crime," Contemporary Economic Policy, Western Economic Association International, vol. 28(2), pages 145-161, April.
    12. Altindag, Duha T., 2012. "Crime and unemployment: Evidence from Europe," International Review of Law and Economics, Elsevier, vol. 32(1), pages 145-157.
    13. Fajnzylber, Pablo & Lederman, Daniel & Loayza, Norman, 2002. "What causes violent crime?," European Economic Review, Elsevier, vol. 46(7), pages 1323-1357, July.
    14. Bedard, Kelly & Helland, Eric, 2004. "The location of women's prisons and the deterrence effect of "harder" time," International Review of Law and Economics, Elsevier, vol. 24(2), pages 147-167, June.
    15. Povilas Lastauskas & Eirini Tatsi, 2013. "Spatial Nexus in Crime and unemployment in Times of crisis: Evidence from Germany," Cambridge Working Papers in Economics 1359, Faculty of Economics, University of Cambridge.
    16. Philip A. Curry & Anindya Sen & George Orlov, 2016. "Crime, apprehension and clearance rates: Panel data evidence from Canadian provinces," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(2), pages 481-514, May.
    17. Lucia dalla Pellegrina & Giorgio Di Maio & Donato Masciandaro & Margherita Saraceno, 2020. "Organized crime, suspicious transaction reporting and anti-money laundering regulation," Regional Studies, Taylor & Francis Journals, vol. 54(12), pages 1761-1775, December.
    18. Tadashi Yamada, 1985. "The Crime Rate and the Condition of the Labor Market: A Vector Autoregressive Model," NBER Working Papers 1782, National Bureau of Economic Research, Inc.
    19. Lauridsen, Jørgen T. & Zeren, Fatma & Ari, Ay?E, 2015. "Is Crime in Turkey Economically Rational?/¿Es económicamente racional el crimen en Turquía?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 33, pages 37-52, Enero.
    20. McCarthy, Killian J. & van Santen, Peter & Fiedler, Ingo, 2015. "Modeling the money launderer: Microtheoretical arguments on anti-money laundering policy," International Review of Law and Economics, Elsevier, vol. 43(C), pages 148-155.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    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:100:y:2001:i:2:p:381-427. 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.