Bias Corrections for Two-Step Fixed Effects Panel Data Estimators

By Iv'an Fern'andez-Val, F. Vella. Social Science Research Network, 2007

84 citations recorded by the source corpus.

This paper introduces large-T bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These models include systems of equations with limited dependent variables and unobserved individual effects, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time varying heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control variable from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control variable accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two-step nature of the procedure. As these two-step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/944b3b11aacef38e2225256ed1ea642f79349c4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Split-Panel Jackknife Estimation of Fixed-Effect Models (2015, methodology): “Both types of complications are known to affect the expression of the incidental-parameter bias—see Bun and Kiviet (2006) and Fernández-Val and Vella (2011), respectively—but pose no additional difficulty for the jackknife.”
Individual and time effects in nonlinear panel models with large N , T (2013, background): “12 In the working paper version, Fernández-Val and Weidner (2015a), we also consider inference conditional on the unobserved effects by assuming that {αi}N and {γt }T are deterministic sequences.”
Bias and consistency in three-way gravity models (2019, methodology): “Turning to three-way models, Hinz, Stammann, and Wanner (2019) have recently developed bias corrections for dynamic three-way probit and logit models based on asymptotics suggested by Fernández-Val and Weidner (2018) where all three panel dimensions grow at the same rate.”
Fixed Effects Estimation of Large-TPanel Data Models (2018, background): “Fernández-Val & Vella (2011) derived bias corrections for two-step estimators of selection and other control variable models with unobserved individual effects in both steps.”