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Informational Content of Factor Structures in Simultaneous Binary Response Models

Shakeeb Khan (), Arnaud Maurel () and Yichong Zhang
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Shakeeb Khan: Boston College, Postal: Dept. of Economics, Boston College, Chestnut Hill, MA 02467 USA
Yichong Zhang: Singapore Management University

No 985, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these systems. In particular, we show that an exclusion restriction, requiring an explanatory variable in the outcome equation to be excluded from the treatment equation, is no longer necessary for identification. Under such settings, we propose a rank estimator for both the factor loading and the causal effect parameter that are root-n consistent and asymptotically normal. The estimator’s finite sample properties are evaluated through a simulation study. We also establish identification results in models with more general factor structures, that are characterized by nonparametric functional forms and multiple idiosyncratic shocks.

Keywords: Factor Structures; Discrete Choice; Causal Effects (search for similar items in EconPapers)
JEL-codes: C14 C31 C35 (search for similar items in EconPapers)
Date: 2019-09-15
New Economics Papers: this item is included in nep-dcm and nep-sea
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