Individual and time effects in nonlinear panel models with large N , T

By Iv'an Fern'andez-Val, Martin P. Weidner. 2013

352 citations recorded by the source corpus.

We derive fixed effects estimators of parameters and average partial effects in (possibly dynamic) nonlinear panel data models with individual and time effects. They cover logit, probit, ordered probit, Poisson and Tobit models that are important for many empirical applications in micro and macroeconomics. Our estimators use analytical and jackknife bias corrections to deal with the incidental parameter problem, and are asymptotically unbiased under asymptotic sequences where N/T converges to a constant. We develop inference methods and show that they perform well in numerical examples.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/b4db0c2870fa9069de321b0d202429e6c7f092e0

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

Gravity models, PPML estimation and the bias of the robust standard errors (2019, positive): “Following Fernandez-Val and Weidner (2016) the PPML estimator for the slope parameters can be written as(1)”
Bias and consistency in three-way gravity models (2019, methodology): “As with the models considered in Fernández-Val and Weidner (2016), the origins of this bias have to do with the rate at which the estimated incidental parameters α̂i and γ̂j converge to their true values α0i and γ0j .”
Split-Panel Jackknife Estimation of Fixed-Effect Models (2015, positive): “In a recent paper, Fernández-Val and Weidner (2013) argue that, under regularity conditions, the introduction of time dummies in a class of linear-index models can be successfully handled by a small modification of the jackknife procedures discussed here.”
DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS (2017, background): “However, one requires additional stationarity assumptions over t and homogeneity assumptions across i to justify the Jackknife correction and to show that β̂ J has the same limiting distribution as β̂∗ in Corollary 4.5; see Fernández-Val & Weidner (2013) for more details.”
Estimating the effects of non‐discriminatory trade policies within structural gravity models (2017, neutral): “However, in terms of inference, we have nothing to add to Fernández-Val and Weidner (2016, 2018) and Weidner and Zylkin (2019), who focus on and demonstrate consistency, biases and inference of the PPML estimator with alternative sets of fixed effects.”
Quantiles via moments (2019, neutral): “…other quantile regression estimators for fixed effects models (see, e.g., Kato, Galvão, and Montes-Rojas, 2012), but it may be possible to address it using bias correction methods of the type developed by Hahn and Newey (2004), Dhaene and Jochmans (2015), and Fernández-Val and Weidner (2016).”
Bias Corrections for Probit and Logit Models with Two-way Fixed Effects (2016, methodology): “The commands implement the analytical and jackknife corrections of Hahn and Newey (2004) and Fernandez-Val (2009), and the split-panel correction of Dhaene and Jochmans (2015).”
Fixed Effects Estimation of Large-TPanel Data Models (2018, methodology): “Fernández-Val & Weidner (2016) extended the ABC to models with unobserved individual and time effects.”