A panel dataset satisfies marginal homogeneity if the time-specific marginal distributions are homogeneous or time-invariant. Marginal homogeneity is relevant in economic settings such as dynamic discrete games. In this paper, we propose several tests for the hypothesis of marginal homogeneity and investigate their properties. We consider an asymptotic framework in which the number of individuals \(n\) in the panel diverges, and the number of periods \(T\) is fixed. We implement our tests by comparing a studentized or non-studentized \(T\)-sample version of the Cramer-von Mises statistic with a suitable critical value. We propose three methods to construct the critical value: asymptotic approximations, the bootstrap, and time permutations. We show that the first two methods result in asymptotically exact hypothesis tests. The permutation test based on a non-studentized statistic is asymptotically exact when \(T=2\), but is asymptotically invalid when \(T>2\). In contrast, the permutation test based on a studentized statistic is always asymptotically exact. Finally, under a time-exchangeability assumption, the permutation test is exact in finite samples, both with and without studentization.

This paper considers a general class of relaxations of the unconfoundedness assumption. This class includes several prominent approaches as special cases, including that of Rosenbaum (1987) and the related marginal sensitivity model of Tan (2006). This class therefore allows us to precisely compare and contrast these previously disparate relaxations. We use this class to derive a variety of new identification results which can be used to assess sensitivity to unconfoundedness. This includes identification results which have been previously unknown even in the special cases.

This paper considers the inference problem for intersection bounds, a class of identified sets derived from intersecting multiple bounds, such as treatment effect bounds obtained through the monotone IV approach. I focus on settings where these bounds are close to each other—a scenario often arising when instruments induce small variation in counterfactual mean outcomes, potentially resulting in limited identifying power. I develop a new two-stage conditional inference procedure. The first stage tests and combines bounds that are close to each other. The second stage devises a conditional critical value for inference based on those aggregated bounds. This new approach improves upon the least-favorable approach and the conditional approach (Andrews, Roth, and Pakes, 2023) when bounds are near each other in a local alternative sequence.

In this paper, we investigate the stationarity of state variables in the estimation of dynamic discrete choice models. By incorporating the steady state as an additional model constraint, we propose a new estimation method that can deliver significant efficiency gains for structural parameters in dynamic discrete choice models.