We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price–dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.

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...

We axiomatize decreasing impatience (DI) in a discrete-time setting, as originally discussed by Prelec, and formulate a class of recursively-defined discounting functions that conform to DI. The recursive formulation is used to analyze the Ramsey growth problem using dynamic programming techniques.