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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

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  • Hoogerheide, Lennart F.
  • Kaashoek, Johan F.
  • van Dijk, Herman K.

Abstract

Likelihoods and posteriors of instrumental variable regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating such contours using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be 'close' to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network that approximates the target density accurately. The methods are tested on a set of illustrative models. The results indicate the feasibility of the neural network approach.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 139 (2007)
Issue (Month): 1 (July)
Pages: 154-180

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Handle: RePEc:eee:econom:v:139:y:2007:i:1:p:154-180

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Web page: http://www.elsevier.com/locate/jeconom

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  1. ZELLNER, Arnold & BAUWENS, Luc & VAN DIJK, Herman K., . "Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods," CORE Discussion Papers RP -796, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Strachan, R.W. & van Dijk, H.K., 2004. "Improper priors with well defined Bayes Factors," Econometric Institute Research Papers EI 2004-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Kleibergen, F.R. & van Dijk, H.K., 1997. "Bayesian Simultaneous Equations Analysis using Reduced Rank Structures," Econometric Institute Research Papers EI 9714/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Angrist, Joshua D & Krueger, Alan B, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 106(4), pages 979-1014, November.
  5. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-89, July.
  6. Charles S. Bos & Ronald J. Mahieu & Herman K. Van Dijk, 2000. "Daily exchange rate behaviour and hedging of currency risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 15(6), pages 671-696.
  7. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  8. Bauwens, L. & Bos, C.S. & van Dijk, H.K. & van Oest, R.D., 2003. "Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods," Econometric Institute Research Papers EI 2003-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, Elsevier, vol. 4(1), pages 17-46, January.
  10. Frank Kleibergen & Eric Zivot, 1998. "Bayesian and Classical Approaches to Instrumental Variables Regression," Econometrics, EconWPA 9812002, EconWPA.
  11. Kleibergen, Frank & van Dijk, Herman K., 1994. "On the Shape of the Likelihood/Posterior in Cointegration Models," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 514-551, August.
  12. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
  13. Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
  14. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, Econometric Society, vol. 57(6), pages 1317-39, November.
  15. Sylvia Fruhwirth-Schnatter, 2004. "Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 143-167, 06.
  16. Paap, Richard & van Dijk, Herman K, 2003. "Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 21(4), pages 547-63, October.
  17. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
  18. DREZE, Jacques H., . "Bayesian limited information analysis of the simultaneous equations model," CORE Discussion Papers RP -300, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  19. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, Econometric Society, vol. 46(1), pages 1-19, January.
  20. DREZE, Jacques H., . "Bayesian regression analysis using poly-t densities," CORE Discussion Papers RP -316, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  21. van Dijk, H. K. & Kloek, T., 1980. "Further experience in Bayesian analysis using Monte Carlo integration," Journal of Econometrics, Elsevier, Elsevier, vol. 14(3), pages 307-328, December.
  22. Chao, J. C. & Phillips, P. C. B., 1998. "Posterior distributions in limited information analysis of the simultaneous equations model using the Jeffreys prior," Journal of Econometrics, Elsevier, Elsevier, vol. 87(1), pages 49-86, August.
  23. Maddala, G S, 1976. "Weak Priors and Sharp Posteriors in Simultaneous Equation Models," Econometrica, Econometric Society, Econometric Society, vol. 44(2), pages 345-51, March.
  24. Hoogerheide, L.F. & Kaashoek, J.F. & van Dijk, H.K., 2004. "Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models," Econometric Institute Research Papers EI 2004-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  25. P. Damlen & J. Wakefield & S. Walker, 1999. "Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 331-344.
  26. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198773139, October.
  27. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
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