Motivated by the question of how one should evaluate professional election forecasters, we study a novel dynamic mechanism design problem without transfers. A principal who wishes to hire only high-quality forecasters is faced with an agent of unknown quality. The agent privately observes...

We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility, given partial and differential information...

We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility given partial and differential information about...

In an environment that features second-degree price discrimination, this paper fully characterizes the set of surplus divisions that can arise from all possible information consumers have about their valuation. By extending the techniques developed in a companion paper (Yang, 2019a), I show that...

Several recent empirical papers assert that the decision to disclose an earnings forecast shortly before the actual earnings announcement reveals only short-term information and is therefore unlikely to entail proprietary costs. Using a simple dynamic model of voluntary disclosure, we show that...

We consider a cross-calibration test of predictions by multiple potential experts in a stochastic environment. This test checks whether each expert is calibrated conditional on the predictions made by other experts. We show that this test is good in the sense that a true expert - one informed of...

We document that analysts cater to short-term investors by issuing optimistic target prices. Catering dominates among analysts at brokers without an investment banking arm as they face lower reputational cost. The market does not see through the analyst catering activity and their forecasts lead...

We provide a general framework to consider the effects of algorithmic demand and supply prediction in matching platforms, such as Task Rabbit for labor markets or Amazon for goods markets, where a significant proportion of matches are repeat transactions. We show that algorithmic repeat match...