We investigate the testable implications of the theory of stable matchings. We provide a characterization of the matchings that are rationalizable as stable matchings when agents' preferences are unobserved. The characterization is a simple nonparametric test for stability, in the tradition of revealed preference tests. We also characterize the observed stable matchings when monetary transfers are allowed and the stable matchings that are best for one side of the market: extremal stable matchings. We find that the theory of extremal stable matchings is observationally equivalent to requiring that there be a unique stable matching or that the matching be consistent with unrestricted monetary transfers.

We develop a new estimation methodology for dynamic optimization models with unobserved shocks and deterministic accumulation of the observed state variables. Investment models are an important example of such models. Our pairwise-difference approach exploits two common features of these models: (1) the monotonicity of the agent's decision (policy) function in the shocks, conditional on the observed state variables; and (2) the state-contingent nature of optimal decision making which implies that, conditional on the observed state variables, the variation in observed choices across agents must be due to randomness in the shocks across agents. We illustrate our procedure by estimating a dynamic trading model for the milk production quota market in Ontario, Canada.

We assess empirically the effects of the winner's curse which, in common-value auctions, counsels more conservative bidding as the number of competitors increases. First, we construct an econometric model of an auction in which bidders' preferences have both common- and private-value components, and propose a new monotone quantile approach which facilitates estimation of this model. Second, we estimate the model using bids from procurement auctions held by the State of New Jersey. For a large subset of these auctions, we find that median procurement costs rise as competition intensifies. In this setting, then, asymmetric information overturns the common economic wisdom that more competition is always desirable.

We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate datasets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone re-arrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.

Abstract We present evidence consistent with time-varying risk preferences among automobile drivers. Exploiting a unique data set of agents' high-frequency driving behavior collected by a mobile phone application, we show that drivers drive more conservatively following near-miss accidents. In a preferred specification, a near-miss triggers a reduction in driving distance of 12.98 kilometers, in-car cell phone use by more than 100%, and highway use by 43.24%. Structural estimation results indicate that such changes in behavior are consistent with an increase in risk aversion of 10.54% to 43.77% and a reduction in annual insurance cost amounting to 2.04% to 3.31% of the average car insurance premium.

Exploiting a rich panel data set on anti-ulcer drug prescriptions, we measure the effects of uncertainty and learning in the demand for pharmaceutical drugs. We estimate a dynamic matching model of demand under uncertainty in which patients learn from prescription experience about the effectiveness of alternative drugs. Unlike previous models, we allow drugs to have distinct symptomatic and curative effects, and endogenize treatment length by allowing drug choices to affect patients' underlying probability of recovery. We find that drugs' rankings along these dimensions differ, with high symptomatic effects for drugs with the highest market shares and high curative effects for drugs with the greatest medical efficacy. Our results also indicate that while there is substantial heterogeneity in drug efficacy across patients, learning enables patients and their doctors to dramatically reduce the costs of uncertainty in pharmaceutical markets.

We estimate an equilibrium model of decision making in the US Supreme Court that takes into account both private information and ideological differences between justices. We measure the value of information in the court by the probability that a justice votes differently from how she would have voted without case-specific information. Our results suggest a sizable value of information: in 44 percent of cases, justices' initial leanings are changed by their personal assessments of the case. Our results also confirm the increased politicization of the Supreme Court in the last quarter century. Counterfactual simulations provide implications for institutional design. (JEL D72, D82, D83, K10)

To investigate whether secondary markets aid or harm durable goods manufacturers, we build a dynamic model of durable goods oligopoly with transaction costs in the secondary market. Calibrating model parameters using data from the US automobile industry, we find the net effect of opening the secondary market is to decrease new car manufacturers' profits by 35 percent. Counterfactual scenarios in which the size of the used good stock decreases, such as when products become less durable, when the number of firms decreases, or when firms can commit to future production levels, increase the profitability of opening the secondary market. (JEL L13, L25, L62, L81)

This paper considers the measurement of consumer loss aversion in product markets. We introduce a test based on a “substitution effect,” focusing on how the end of a sale affects sales not of the good itself, but a substitute good. Such an effect cannot be easily confounded with consumer stockpiling. Using a unique dataset from an online hardware retailer, we find evidence consistent with consumer loss aversion. Moreover, we find that less experienced consumers suffer a more prominent loss aversion bias compared to more experienced consumers.

This paper proposes a new semi‐parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining convex‐analytic feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requiring any shape restrictions for the distribution of the random utility shocks. These inequalities point identify model parameters under straightforward assumptions on the covariates. We propose a consistent estimator based on these inequalities.