Stochastic sequential bargaining models (Merlo and Wilson (1995, 1998)) have found wide applications in different fields including political economy and macroeconomics due to their flexibility in explaining delays in reaching an agreement. This paper presents new results in nonparametric identification and estimation of such models under different data scenarios.

This paper studies entry and capacity decisions by dialysis providers in the United States. We estimate a structural model where providers make continuous strategic choices of capacity based on their private information about own costs and knowledge of the distribution of competitors’ private information. We evaluate the impact on the market structure and providers’ profits under counterfactual regulatory policies that increase the costs or reduce the payment per unit of capacity. We find that these policies reduce the market capacity as measured by the number of dialysis stations. However, the downward-sloping reaction curve shields some providers from negative profit shocks in certain markets. The paper also has a methodological contribution in that it proposes new estimators for Bayesian games with continuous actions.

We develop and estimate a structural model of arbitration, accounting for asymmetric risk attitudes and learning. Using data on public sector wage disputes in New Jersey, we compare the efficiency of two popular arbitration formats: final offer and conventional. We find that although conventional arbitration hinders the transmission of case-relevant information from the disputants to the arbitrator, this format outperforms final offer arbitration by affording discretion to select awards. We also assess how risk attitude differences between the disputants affect imbalances in arbitration outcomes, finding that risk aversion weakens a party?s position in the dispute despite making them more likely to win arbitration.

This paper studies the inference of interaction effects, i.e. the impacts of players' actions on each other's payoffs, in discrete simultaneous games with incomplete information. We propose an easily implementable test for the signs of state-dependent interaction effects which does not require parametric specifications of players' payoffs, the distributions of their private signals or the equilibrium selection mechanism. The test relies on the commonly invoked assumption that players' private signals are independent conditional on observed states. The procedure is valid in the presence of multiple equilibria and as a by-product we propose a formal test for multiple equilibria in the data-generating process. We provide Monte Carlo evidence of the test's good performance in finite samples. We also implement it to infer the direction of interaction effects in couples' joint retirement decisions using data from the Health and Retirement Study.

In this note we revisit the use of exclusion restrictions in the semiparametric binary choice panel data model introduced in Honore and Lewbel (2002). We show that in a dynamic panel data setting (where one of the pre-determined explanatory variables is the lagged dependent variable), the exclusion restriction in Honore and Lewbel (2002) implicitly re- quires serial independence condition on an observed regressor, that if violated in the data will result in their procedure being inconsistent. We propose a new identification strategy and estimation procedure for the semiparametric binary panel data model under exclusion restrictions that accommodate the serial correlation of observed regressors in a dynamic setting. The new estimator converges at the parametric rate to a limiting normal distribution. This rate is faster than the nonparametric rates of existing alternative estimators for the binary choice panel data model, including the static case in Manski (1987) and the dynamic case in Honore and Kyriazidou (2000).

In online procurement markets, projects are often allocated through a mechanism that allows buyers to take into account a seller’s nonprice characteristics as well as his bid. We design a methodology to recover primitives of the environment in the presence of unobserved seller heterogeneity while accommodating two important features of such markets: buyer-specific choice sets and the high turnover of sellers. We apply our method to data from an online market for programming services, to assess buyers’ welfare gains associated with the globalization enabled by the internet. We find that the internet enables buyers to substantially improve on their outside (local) option; many of the gains arise from access to the international markets.

We point-identify and estimate linear social network models without observing any network links. The required data consist of many small networks of individuals, such as classrooms or villages, with individuals who are each observed only once. We apply our estimator to data from Tennessee’s Project STAR (Student-Teacher Achievement Ratio). Without observing the latent network in each classroom, we identify and estimate peer and contextual effects on students’ performance in mathematics. We find that peer effects tend to be larger in bigger classes and that increasing peer effects would significantly improve students’ average test scores in some classes.