Multiple fixed effects in binary response panel data models

Abstract

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An econometric model of network formation with degree heterogeneity

“…This analysis parallels recent findings from the non-linear panel data literature under large-N, large-T asymptotics (e.g., Hahn and Newey, 2004;Arellano and Hahn, 2007). Dzemski (2014), in related work, studies the properties of joint maximum-likelihood applied to the directed network model of Charbonneau (2014). His analysis builds on Fernández-Val and Weidner's (2016) study of non-linear panel data models with both individual-and time-effects.…”

“…Similar results form the basis of conditional maximum likelihood estimators in nonlinear panel data models (Cox, 1958;Chamberlain, 1980). Recently, in independent work, Charbonneau (2014) uses sufficiency arguments to develop conditional estimators for nonlinear models with multiple fixed effects. Her analysis is inspired by empirical studies of international trade, where the introduction of importer and exporter effects is common (e.g., Santos Silva and Tenreyro, 2006).…”

“…In contrast, the results presented here apply to undirected networks. Charbonneau (2014) does not characterize the large sample properties of her estimator.…”

A discrete‐choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

“…This section compares our methodology to alternative estimators for large, nonlinear panels with fixed effects (Charbonneau, 2014;Chen et al, 2014;Fernandez-Val & Weidner, 2016;Sun, 2016). Fernandez-Val and Weidner (2016) study nonlinear panel data models with individual and time fixed effects.…”

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

“…This section compares our methodology to alternative estimators for large, nonlinear panels with fixed effects (Fernandez-Val and Weidner (2016), Charbonneau (2014), Sun (2016), Chen et al (2014)). (2016) is to characterize the bias that arises due to the incidental parameter problem (Neyman and Scott (1948)) and to provide analytical and jackknife bias corrections.…”