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 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...

This dissertation consists of three empirical chapters. The first chapter examines the extent to which real-world agents are rational in making quantitative expectations, an issue over which there is much debate. In this chapter dynamic models for new plant-level survey data are estimated in...

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.

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a...

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively...