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Bayesian Approach and Identification

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  • Kociecki, Andrzej

Abstract

The paper aims at systematic placement of identification concept within Bayesian approach. Pointing to some deficiencies of the standard Bayesian language to describe identification problem we propose several useful characterizations that seem to be intuitively sound and attractive given their potential applications. We offer comprehensive interpretations for them. Moreover we introduce the concepts of uniform, marginal and faithful identification. We argue that all these concepts may have practical significance. Our theoretical development is illustrated with a number of simple examples and one real application i.e. Structural VAR model.

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File URL: http://mpra.ub.uni-muenchen.de/46538/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 46538.

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Date of creation: 25 Apr 2013
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Handle: RePEc:pra:mprapa:46538

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Keywords: Bayesian; Identification;

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  1. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
  2. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Thomas J. Sargent, 2005. "A, B, C’s (And D’s) For Understanding VARS," PIER Working Paper Archive 05-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Edward Barankin, 1960. "Sufficient parameters: Solution of the minimal dimensionality problem," Annals of the Institute of Statistical Mathematics, Springer, vol. 12(2), pages 91-118, June.
  4. Abderrahim Oulhaj & Michel Mouchart, 2003. "Partial sufficiency with connection to the identification problem," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 267-283.
  5. Fabio Canova & Luca Sala, 2006. "Back to square one: identification issues in DSGE models," Computing in Economics and Finance 2006 196, Society for Computational Economics.
  6. Gustafson, Paul, 2009. "What Are the Limits of Posterior Distributions Arising From Nonidentified Models, and Why Should We Care?," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1682-1695.
  7. Christopher A. Sims, 2007. "Thinking about instrumental variables (in Russian)," Quantile, Quantile, issue 2, pages 83-94, March.
  8. Maddala, G S, 1976. "Weak Priors and Sharp Posteriors in Simultaneous Equation Models," Econometrica, Econometric Society, vol. 44(2), pages 345-51, March.
  9. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
  10. Eric Leeper & Tao Zha, 2002. "Empirical analysis of policy interventions," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  11. Ríos-Rull, José-Víctor & Schorfheide, Frank & Fuentes-Albero, Cristina & Kryshko, Maxym & Santaeulàlia-Llopis, Raül, 2012. "Methods versus substance: Measuring the effects of technology shocks," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 826-846.
  12. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
  13. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
  14. Bowden, Roger J, 1973. "The Theory of Parametric Identification," Econometrica, Econometric Society, vol. 41(6), pages 1069-74, November.
  15. Aart Kraay, 2012. "Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 108-128, 01.
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