We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on...

Economics has long studied how consumers respond to the disclosure of information about firms. We study a case in which the disclosed information is unrelated to the product or firm leadership, but which could still potentially affect consumer patronage through the mechanism of repugnance, as...

We propose a general equilibrium model where two special interest groups (SIGs) compete to influence public opinion. Citizens with heterogeneous priors over a binary state of the world receive reports drawn from a continuous message space by a variety of sources. The two opposite SIGs attempt to...

We study the redistributive effects of inflation combining administrative bank data with an information provision experiment during an episode of historic inflation. On average, households are well-informed about prevailing inflation and are concerned about its impact on their wealth; yet, while...

A growing literature uses now widely-available data on beliefs and expectations in the estimation of structural models. In this chapter, we review this literature, with an emphasis on models of individual and household behavior. We first show how expectations data have been used to relax strong...

Financial ties between drug companies and medical researchers are thought to bias studies published in medical journals. To enable readers to account for such bias, most medical journals require authors to disclose potential conflicts of interest. We examine whether disclosure reduces article...

We study how investors respond to inflation combining a customized survey experiment with trading data at a time of historically high inflation. Investors' beliefs about the stock return-inflation relation are very heterogeneous in the cross section and on average too optimistic. Moreover, many...

For decades credit rating agencies were viewed as trusted arbiters of creditworthiness and their ratings as important tools for managing risk. The common narrative is that the value of ratings was compromised by the evolution of the industry to a form where issuers pay for ratings. In this paper...

What do machines learn, and why? To answer these questions we import models of human cognition into machine learning. We propose two ways of modeling machine learners based on this join: feasibility-based and cost-based machine learning. We evaluate and estimate our models using a deep learning...

We find evidence suggesting that surveys of professional forecasters are biased by strategic incentives. First, we find that individual forecasts overreact to idiosyncratic information but underreact to common information. Second, we show that this bias is not present in forecasts data that is...