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state-observation sampling and the econometrics of learning models

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  • Calvet, Laurent-Emmanuel

    ()
    (HEC Paris)

  • Czellar , Veronika

    ()
    (HEC Paris)

Abstract

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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Bibliographic Info

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

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Postal: HEC Paris, 78351 Jouy-en-Josas cedex, France
Web page: http://www.hec.fr/
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Related research

Keywords: hidden markov model; particle filter; state-observation sampling; learning; indirect inference; forecasting; state space model; value at risk;

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References

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