Learning, Large Deviations and Rare Events / Jess Benhabib, Chetan Dave.

Format:

Book

Author/Creator:

Benhabib, Jess.

Contributor:

National Bureau of Economic Research.

Dave, Chetan.

Series:

Working Paper Series (National Bureau of Economic Research) no. w16816.

NBER working paper series no. w16816

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2011.

Summary:

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.

Notes:

Print version record

February 2011.