Learning, Large Deviations and Rare Events

Jess Benhabib and Chetan Dave

No 16816, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We examine the role of generalized constant gain stochastic gradient (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat tailed distribution even though the model is driven by thin tailed exogenous stochastic processes. We characterize these large deviations that are driven by sequences of consistently low or consistently high shocks. We then apply our model to the canonical asset-pricing model. We demonstrate that the tails of the stationary distribution of the price-dividend ratio will follow a power law.

JEL-codes: D83 D84 (search for similar items in EconPapers)
Date: 2011-02
Note: EFG
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published as Jess Benhabib & Chetan Dave, 2014. "Learning, Large Deviations and Rare Events," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), July.

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Journal Article: Learning, Large Deviations and Rare Events (2014)
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