State-Observation Sampling and the Econometrics of Learning Models

45 Pages Posted: 26 May 2011

See all articles by Laurent E. Calvet

Laurent E. Calvet

SKEMA Business School; CEPR

Veronika Czellar

SKEMA Business School

Date Written: May 16, 2011

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.

Keywords: Hidden Markov model, particle filter, learning, indirect inference, forecasting, state space model, value at risk

JEL Classification: C11, C13, C14, C15, C32, C52, C53, G12

Suggested Citation

Calvet, Laurent E. and Czellar, Veronika, State-Observation Sampling and the Econometrics of Learning Models (May 16, 2011). Available at SSRN: https://ssrn.com/abstract=1847646 or http://dx.doi.org/10.2139/ssrn.1847646

Laurent E. Calvet (Contact Author)

SKEMA Business School ( email )

5 Quai Marcel Dassault
Suresnes, 92150
France

CEPR ( email )

33 Great Sutton Street
London, EC1V 0DX
United Kingdom

Veronika Czellar

SKEMA Business School ( email )

5 quai Marcel Dassault
Suresnes, 92156
France

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