Job Market Talk: "Unobserved Clusters of Time-Varying Heterogeneity in Nonlinear Panel Data Models," Martin Mugnier, CREST, ENSAE Paris, Institut Polytechnique de Paris
Martin Mugnier, CREST, ENSAE Paris, Institut Polytechnique de Paris
Martin Mugnier is a Phd Candidate from CREST, ENSAE Paris, Institut Polytechnique de Paris. Prior to attending CREST, Martin received his BSc in Economics from the University of Paris 1 Panthéon-Sorbonne & ENS Paris-Saclay. His research focuses on Econometrics with a focus on the identification and estimation of nonlinear panel data models.
Abstract: In studies based on longitudinal data, researchers often assume time-invariant unobserved heterogeneity or linear in-parameters conditional expectations. Violation of these assumptions may lead to poor counterfactuals. I study the identification and estimation of a large class of nonlinear grouped fixed effects (NGFE) models where the relationship between observed covariates and cross-sectional unobserved heterogeneity is left unrestricted but the latter only takes a restricted number of paths over time. I show that the corresponding “clusters” and the nonparametrically specified link function can be point-identified when both dimensions of the panel are large. I propose a semiparametric NGFE estimator and establish its large sample properties in popular binary and count outcome models. Distinctive features of the NGFE estimator are that it is asymptotically normal unbiased at parametric rates, and it allows for the number of periods to grow slowly with the number of cross-sectional units. Monte Carlo simulations suggest good finite sample performance. I apply this new method to revisit the so-called inverted-U relationship between product market competition and innovation. Allowing for clustered patterns of time-varying unobserved heterogeneity leads to a less pronounced inverted-U relationship.
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