We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

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 extend the correspondences between adaptive learning algorithms and the Kalman filter to formulations with time-varying gains. Our correspondences hold exactly, in a computational implementation sense, and we discuss how they relate to previous approximate correspondences found in the literature.

Subjects who overestimate their performance in experimental tasks unrelated to travel are less willing to insure against failing in the task and also less inclined to buy travel insurance. This suggests intrinsic optimism influences insurance demand and diminishes adverse selection.

People have the natural tendency to be optimistic and believe that good outcomes in the future are more likely, but also want to avoid overestimation that could result in bad decision-making. Brunnermeier, Brunnermeier and Parker (2005) and Brunnermeier et al. (2007) established an optimal...