Publications

We exhibit a natural environment, social learning among heterogeneous agents, where even slight misperceptions can have a large negative impact on long-run learning outcomes. We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents’ actions over time, where agents’ actions depend not only on their beliefs about the state but also on their idiosyncratic types (e.g., tastes or risk attitudes). When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, we show, first, that even arbitrarily small amounts of misperception about the type distribution can generate extreme breakdowns of information aggregation, where in the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state.  Second, any misperception of the type distribution leads long-run beliefs and behavior to vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Third, we show that how fragile information aggregation is against misperception depends on the richness of agents’ payoff-relevant uncertainty; a design implication is that information aggregation can be improved by simplifying agents’ learning environment. The key feature behind our findings is that agents’ belief-updating becomes “decoupled” from the true state over time. We point to other environments where this feature is present and leads to similar fragility results.

We study reward-based crowdfunding campaigns, a new class of dynamic contribution games where consumption is exclusive. Two types of backers participate: buyers want to consume the product while donors just want the campaign to succeed. The key tension is one of coordination between buyers, instead of free-riding. Donors can alleviate this coordination risk. We analyze a dynamic model of crowdfunding and demonstrate that its predictions are consistent with high-frequency data collected from Kickstarter. We compare the Kickstarter mechanism to alternative platform designs and evaluate the value of dynamically arriving information. We extend the model to incorporate social learning about quality.

What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback? For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy. Our estimators are shown to have lowest variance in a wide class of estimators, achieving variance reduction relative to standard estimators. We then apply our estimators to improve advertisement design by a major advertisement company. Consistent with the theoretical result, our estimators allow us to improve on the existing bandit algorithm with more statistical confidence compared to a state-of-theart benchmark.

The steady application of Quantitative Easing (QE) has been followed by big and non-monotonic effects on international asset prices and international capital flows. These are difficult to explain in conventional models, but arise naturally in a model with collateral. This paper develops a general-equilibrium framework to explore QE’s international transmission involving an advanced economy (AE) and an emerging market economy (EM) whose assets have less collateral capacity. Capital flows arise as a result of international sharing of scarce collateral. The crucial insight is that private AE agents adjust their portfolios in different ways in response to QE, conditional on whether they are (i) fully leveraged, (ii) partially leveraged or (iii) unleveraged. These portfolio shifts of international assets can diminish or even reverse the effectiveness of ever-larger QE interventions on asset prices. The model provides a simultaneous interpretation of several important stylized facts associated with QE. 

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips, 1986, 1998; Sun, 2004, 2014), the usual t test and HAC standardized test fail to control size as the sample size n→∞ in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors K→∞, all three statistics, including the HAR test, diverge and fail to control size as n→∞. These findings are relevant to high dimensional nonstationary time series regressions.

We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly from an econometric perspective and importantly for global policy analysis, it is shown that despite the substantial differences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO₂) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO₂. 

We define and investigate a property of mechanisms that we call “strategic simplicity,” and that is meant to capture the idea that, in strategically simple mechanisms, strategic choices require limited strategic sophistication. We define a mechanism to be strategically simple if choices can be based on first-order beliefs about the other agents’ preferences and first-order certainty about the other agents’ rationality alone, and there is no need for agents to form higher-order beliefs, because such beliefs are irrelevant to the optimal strategies. All dominant strategy mechanisms are strategically simple. But many more mechanisms are strategically simple. In particular, strategically simple mechanisms may be more flexible than dominant strategy mechanisms in the bilateral trade problem and the voting problem.

We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore differs from the one that is optimal under correct specification. To formally show the near-optimality of these CIs, we develop asymptotic efficiency bounds for inference in the locally misspecified GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more.