Dr Adam Rosen

We study inference in complete information games with discrete strategy spaces.

We study inference in complete information games with discrete strategy spaces. Unlike binary games, we allow for rich strategy spaces and we only assume that they are ordinal in nature.

This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions.

We study an extension of a treatment effect model in which an observed discrete classifier indicates which one of a set of counterfactual processes occurs, each of which may result in the realization of several endogenous outcomes.

This paper demonstrates the use of bounds analysis for empirical models of market structure that allow for multiple equilibria.

We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Manski (1975, 1985).

Models of simultaneous discrete choice may be incomplete, delivering multiple values of outcomes at certain values of the latent variables and co-variates, and incoherent, delivering no values.

Recent research underscores the sensitivity of conclusions drawn from the application of econometric methods devised for quantitative outcome variables to data featuring ordinal outcomes. The issue is particularly acute in the analysis of happiness data, for which no natural cardinal scale exists, and which is thus routinely collected by ordinal response. With ordinal responses, comparisons of means across different populations and the signs of OLS regression coefficients have been shown to be sensitive to monotonic transformations of the cardinal scale onto which ordinal responses are mapped.

In this paper we analyze a discrete choice model for partially ordered alternatives. The alternatives are differentiated along two dimensions, the first an unordered “horizontal” dimension, and the second an ordered “vertical” dimension.

This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables.

This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables.

This paper studies identification and estimation of the distribution of bidder valuations in an incomplete model of English auctions. As in Haile and Tamer (2003) bidders are assumed to (i) bid no more than their valuations and (ii) never let an opponent win at a price they are willing to beat. Unlike the model studied by Haile and Tamer (2003), the requirement of independent private values is dropped, enabling the use of these restrictions on bidder behavior with affiliated private values, for example through the presence of auction specifi…c unobservable heterogeneity. In addition, a semiparametric index restriction on the effect of auction-specifi…c observable heterogeneity is incorporated, which, relative to nonparametric methods, can be help- ful in alleviating the curse of dimensionality with a moderate or large number of covariates. The identification analysis employs results from Chesher and Rosen (2017) to characterize identified sets for bidder valuation distributions and functionals thereof.

Generalized Instrumental Variable Models

This paper develops characterizations of identifi…ed sets of structures and structural features for complete and incomplete models involving continuous or discrete variables.

Advances in the study of partial identification allow applied researchers to learn about parameters of interest without making assumptions needed to guarantee point identification. We discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing information to applied researchers that can help them employ these methods in practice.

The authors discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing information to applied researchers that can help them employ these methods in practice.

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous and/or discrete variables.