We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

Three key variables determine the fair value estimates for financial assets: the current and future payoffs provided by the asset, the current and future short-term interest rates controlled by central banks, and the current and future excess returns - or short-term risk premia - required by...

Social learning (SL) is a behavioral model in which expectations and the resulting aggregate dynamics stem from the interactions of a large amount of heterogeneous agents. Nonetheless, this framework has so far lacked micro-foundations and a general-solution method. This paper bridges these two...

This paper proposes an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, rational or...

Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and predictable by forecast revisions. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have...

With rising customer expectations and increasing computational potential, many transportation services face real-time decision making in stochastic and dynamic environments. They often need to find and adapt complex plans that are effective now but also flexible with respect to future...

This paper studies wage effects and job mobility as a result of skill mismatch in worker- occupation pairs. I develop a Roy model in which learning on the job induces workers to shift more time towards job-specific activities. Using a short task panel containing data on worker’s time...