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state-observation sampling and the econometrics of learning models

  • Calvet, Laurent-Emmanuel

    ()

    (HEC Paris)

  • Czellar , Veronika

    ()

    (HEC Paris)

Author's abstract. In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the incomplete-information equilibrium models considered in financial economics. In this paper, we make two contributions to the learning literature. First, we introduce a new filtering method, the state-observation sampling (SOS) filter, for general state-space models with intractable observation densities. Second, we develop an indirect inference-based estimator for a large class of incomplete-information economies. We demonstrate the good performance of these techniques on an asset pricing model with investor learning applied to over 80 years of daily equity returns.

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Paper provided by HEC Paris in its series Les Cahiers de Recherche with number 947.

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Length: 46 pages
Date of creation: 01 May 2011
Date of revision:
Handle: RePEc:ebg:heccah:0947
Contact details of provider: Postal: HEC Paris, 78351 Jouy-en-Josas cedex, France
Web page: http://www.hec.fr/

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  1. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
  2. Laurent E. Calvet & Adlai J. Fisher & Samuel B. Thompson, 2004. "Volatility Comovement: A Multifrequency Approach," NBER Technical Working Papers 0300, National Bureau of Economic Research, Inc.
  3. Michael S. Johannes & Nicholas G. Polson & Jonathan R. Stroud, 2009. "Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2559-2599, July.
  4. Pablo A. Guerron-Quintana & Martin Uribe & Juan Rubio-Ramirez & Jesús Fernández-Villaverde, 2009. "Risk Matters: The Real E¤ects of Volatility Shocks," 2009 Meeting Papers 237, Society for Economic Dynamics.
  5. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 49-83.
  6. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
  7. Jes�s Fern�ndez-Villaverde & Juan F. Rubio-Ram�rez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1059-1087.
  8. Timmermann, Allan, 1996. "Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning," Review of Economic Studies, Wiley Blackwell, vol. 63(4), pages 523-57, October.
  9. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2006. "Analysis of high dimensional multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 134(2), pages 341-371, October.
  10. Guidolin, Massimo & Timmermann, Allan G, 2001. "Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities," CEPR Discussion Papers 3005, C.E.P.R. Discussion Papers.
  11. Lettau, Martin & Ludvigson, Sydney & Wachter, Jessica, 2006. "The Declining Equity Premium: What Role Does Macroeconomic Risk Play?," CEPR Discussion Papers 5519, C.E.P.R. Discussion Papers.
  12. Van Nieuwerburgh, Stijn & Veldkamp, Laura, 2006. "Learning asymmetries in real business cycles," Journal of Monetary Economics, Elsevier, vol. 53(4), pages 753-772, May.
  13. Stephen G. Cecchetti & Pok-sang Lam & Nelson C. Mark, 1998. "Asset Pricing with Distorted Beliefs: Are Equity Returns Too Good To Be True?," NBER Working Papers 6354, National Bureau of Economic Research, Inc.
  14. Christophe Andrieu & Arnaud Doucet, 2002. "Particle filtering for partially observed Gaussian state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 827-836.
  15. Calvet, Laurent E. & Fisher, Adlai J., 2007. "Multifrequency news and stock returns," Journal of Financial Economics, Elsevier, vol. 86(1), pages 178-212, October.
  16. Bruno Biais & Peter Bossaerts & Chester Spatt, 2010. "Equilibrium Asset Pricing and Portfolio Choice Under Asymmetric Information," Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1503-1543, April.
  17. Paul Fearnhead & Peter Clifford, 2003. "On-line inference for hidden Markov models via particle filters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 887-899.
  18. Lubos Pastor & Pietro Veronesi, 2005. "Technological Revolutions and Stock Prices," NBER Working Papers 11876, National Bureau of Economic Research, Inc.
  19. Giorgio Calzolari & Gabriele Fiorentini & Enrique Sentana, 2004. "Constrained Indirect Estimation," Review of Economic Studies, Wiley Blackwell, vol. 71(4), pages 945-973, October.
  20. Veronika Czellar & Elvezio Ronchetti, 2010. "Accurate and robust tests for indirect inference," Biometrika, Biometrika Trust, vol. 97(3), pages 621-630.
  21. Calvet, Laurent & Fisher, Adlai, 2001. "Forecasting multifractal volatility," Journal of Econometrics, Elsevier, vol. 105(1), pages 27-58, November.
  22. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January.
  23. Timmermann, Allan G, 1993. "How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 108(4), pages 1135-45, November.
  24. Lars Peter Hansen, 2007. "Beliefs, Doubts and Learning: Valuing Macroeconomic Risk," American Economic Review, American Economic Association, vol. 97(2), pages 1-30, May.
  25. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  26. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
  27. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
  28. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
  29. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  30. Brennan, Michael J. & Xia, Yihong, 2001. "Stock price volatility and equity premium," Journal of Monetary Economics, Elsevier, vol. 47(2), pages 249-283, April.
  31. van Handel, Ramon, 2009. "Uniform time average consistency of Monte Carlo particle filters," Stochastic Processes and their Applications, Elsevier, vol. 119(11), pages 3835-3861, November.
  32. Knut Heggland & Arnoldo Frigessi, 2004. "Estimating functions in indirect inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 447-462.
  33. Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Centre de Recherche en Economie et Statistique.
  34. Nicholas G. Polson & Jonathan R. Stroud & Peter Müller, 2008. "Practical filtering with sequential parameter learning," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 413-428.
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