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Bayesian IV: the normal case with multiple endogenous variables

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  • Cogley, Timothy
  • Startz, Richard

Abstract

We set out a Gibbs sampler for the linear instrumental-variable model withnormal errors and normal priors, and we show how to compute the marginallikelihood.

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Bibliographic Info

Paper provided by Department of Economics, UC Santa Barbara in its series University of California at Santa Barbara, Economics Working Paper Series with number qt40v0x246.

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Date of creation: 01 Sep 2012
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Handle: RePEc:cdl:ucsbec:qt40v0x246

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Keywords: Social and Behavioral Sciences; instrumental variables; bayesian; gibbs sampling;

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  1. John F. Geweke, 1995. "Bayesian reduced rank regression in econometrics," Working Papers, Federal Reserve Bank of Minneapolis 540, Federal Reserve Bank of Minneapolis.
  2. Conley, Timothy G. & Hansen, Christian B. & McCulloch, Robert E. & Rossi, Peter E., 2008. "A semi-parametric Bayesian approach to the instrumental variable problem," Journal of Econometrics, Elsevier, Elsevier, vol. 144(1), pages 276-305, May.
  3. Gao, Chuanming & Lahiri, Kajal, 2000. "MCMC algorithms for two recent Bayesian limited information estimators," Economics Letters, Elsevier, Elsevier, vol. 66(2), pages 121-126, February.
  4. Hoogerheide, Lennart & Kleibergen, Frank & van Dijk, Herman K., 2007. "Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data," Journal of Econometrics, Elsevier, Elsevier, vol. 138(1), pages 63-103, May.
  5. Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variable Regression," Discussion Papers in Economics at the University of Washington, Department of Economics at the University of Washington 0063, Department of Economics at the University of Washington.
  6. HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Arnold Zellner & Tomohiro Ando & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2011. "Instrumental Variables, Errors in Variables, and Simultaneous Equations Models: Applicability and Limitations of Direct Monte Carlo," Tinbergen Institute Discussion Papers, Tinbergen Institute 11-137/4, Tinbergen Institute.
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