Department of Economics, UCSD

Abstract: In repeated games, where both collusive and noncollusive outcomes can be supported as equilibria, it is crucial to understand the likelihood of selection for each type of equilibrium. Controlled experiments have empirically validated a selection criterion for the two-player repeated prisoner’s dilemma: the basin of attraction for always defect. This prediction device uses the game primitives to measure the set of beliefs for which an agent would prefer to unconditionally defect rather than attempt conditional cooperation. This belief measure reflects strategic uncertainty over others’ actions, where the prediction is for noncooperative outcomes when the basin measure is full, and cooperative outcomes when empty. We expand this selection notion to multi-player social dilemmas and experimentally test the predictions, manipulating both the total number of players and the payoff tensions. Our results affirm the model as a tool for predicting long-term cooperation, while also speaking to some limitations when dealing with first-time encounters.

We experimentally document persistence of suboptimal behavior despite ample opportunities to learn from feedback in a canonical updating problem where people suffer from base-rate neglect. Our results provide insights on the mechanisms hindering learning from feedback. Importantly, our results suggest mistakes are more likely to be persistent when they are driven by incorrect mental models that miss or misrepresent important aspects of the environment. Such models induce confidence in initial answers, limiting engagement with and learning from feedback. We substantiate these insights in an alternative scenario where individuals involved in a voting problem overlook the importance of being pivotal. (JEL D83, D91)

Abstract: We present an experimental framework to study the extent to which failures of contingent thinking explain classic anomalies in a broad class of environments, including overbidding in auctions and the Ellsberg paradox. We study environments in which the subject’s choices affect payoffs only in some states, but not in others. We find that anomalies are in large part driven by incongruences between choices in the standard presentation of each problem and a ‘contingent’ presentation, which focuses the subject on the set of states where her actions matter. Additional evidence suggests that this phenomenon is in large part driven by people’s failure to put themselves in states that have not yet happened even though they are made aware that their actions only matter in those states.

In recent years, experiments have documented a new mechanism that leads to failures of profit maximization: the failure of contingent thinking (FCT). This article summarizes key experimental findings, clarifies what constitutes an FCT, and outlines how FCTs can be tested in other environments. Subsequently, we relate FCTs to recent theoretical work on cognitive limitations in behavioral economics. Finally, we connect FCTs to suboptimal behavior documented in applied environments.

Abstract: We find strong empirical evidence that the liquidity yield on government bonds in combination with standard economic fundamentals can well account for nominal exchange rate movements. We find impressive evidence that changes in the liquidity yield are significant in explaining exchange rate changes for all the G10 countries, and we stress that the US dollar is not special in this relationship. We show how these relationships arise out of a canonical two-country New Keynesian model with liquidity returns. Additionally, we find a role for sovereign default risk and currency swap market frictions.

Abstract: We study the finite sample behavior of Lasso-based inference methods such as post–double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso's not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance.