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This paper considers estimation of discrete choice models when agents report their ranking of the alternatives (or some of them) rather than just the utility maximizing alternative. We investigate the parametric conditional rank-ordered Logit model. We show that conditions for identification do not change even if we observe ranking. Moreover, we fill a gap in the literature and show analytically and by Monte Carlo simulations that efficiency increases as we use additional information on the ranking.

We examine the impact of heterogeneous discounting on collusion in dynamic Bertrand competition. We show exactly when collusion can be sustained and how collusion would be organized efficiently with heterogeneous discounting. First, we show that collusion is possible if and only if the average discount factor exceeds a certain threshold, with or without capacity constraints. Next, we identify a dynamic pattern of market share that characterizes efficient collusion and obtain the unique long‐run prediction despite the presence of multiple equilibria. In the long run, the most patient firm and the most impatient firm tend to dominate the market.

We propose an empirical framework for Cournot oligopoly with private information about costs. First, considering a linear demand with a random intercept, we characterize the Bayesian Cournot-Nash equilibrium and determine its testable implications. Then we establish nonparametric identification of the joint distribution of demand and technology shock and firm-specific cost distributions. Finally, we propose a likelihood-based estimation method and apply it to the global crude oil market. Using counterfactuals, we also quantify the effect of firms sharing information about their costs on consumer welfare. We also extend the model to include either firm-specific conduct parameters, nonlinear demand, or selective entry.