Working Paper: NBER ID: w16816
Authors: Jess Benhabib; Chetan Dave
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.
Keywords: No keywords provided
JEL Codes: D83; D84
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| generalized constant gain stochastic gradient (SGCG) learning (C73) | large deviations in an endogenous variable from its rational expectations value (D80) |
| adaptive learning dynamics (C69) | frequency of large deviations (C46) |
| sequences of consistently low or high shocks (C22) | large deviations (C46) |
| adaptive learning dynamics (C69) | fat tails in the distribution of the price-dividend ratio (C46) |
| tail index of the price-dividend ratio (G12) | function of model parameters (C51) |
| optimal gain parameter (H21) | tail index of the price-dividend ratio (G12) |