biasCorr: Asymptotic bias correction after fitting binary choice models with a one-/two-/three-way error component

Description

biasCorr is a post-estimation routine that can be used to substantially reduce the incidental parameter bias problem (Neyman and Scott (1948)) present in nonlinear fixed effects models (see Fernández-Val and Weidner (2018) for an overview). The command applies the analytical bias correction derived by Fernández-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) to obtain bias-corrected estimates of the structural parameters and is currently restricted to binomial with one-, two-, and three-way fixed effects.

Usage

biasCorr(object = NULL, L = 0L, panel.structure = c("classic", "network"))

Value

The function biasCorr returns a named list of classes "biasCorr" and "feglm".

Arguments

object: an object of class "feglm"; currently restricted to binomial.
L: unsigned integer indicating a bandwidth for the estimation of spectral densities proposed by Hahn and Kuersteiner (2011). Default is zero, which should be used if all regressors are assumed to be strictly exogenous with respect to the idiosyncratic error term. In the presence of weakly exogenous regressors, e.g. lagged outcome variables, Fernández-Val and Weidner (2016, 2018) suggest to choose a bandwidth between one and four. Note that the order of factors to be partialed out is important for bandwidths larger than zero (see vignette for details).
panel.structure: a string equal to "classic" or "network" which determines the structure of the panel used. "classic" denotes panel structures where for example the same cross-sectional units are observed several times (this includes pseudo panels). "network" denotes panel structures where for example bilateral trade flows are observed for several time periods. Default is "classic".

References

Czarnowske, D. and A. Stammann (2020). "Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels". ArXiv e-prints.

Fernández-Val, I. and M. Weidner (2016). "Individual and time effects in nonlinear panel models with large N, T". Journal of Econometrics, 192(1), 291-312.

Fernández-Val, I. and M. Weidner (2018). "Fixed effects estimation of large-t panel data models". Annual Review of Economics, 10, 109-138.

Hahn, J. and G. Kuersteiner (2011). "Bias reduction for dynamic nonlinear panel models with fixed effects". Econometric Theory, 27(6), 1152-1191.

Hinz, J., A. Stammann, and J. Wanner (2020). "State Dependence and Unobserved Heterogeneity in the Extensive Margin of Trade". ArXiv e-prints.

Neyman, J. and E. L. Scott (1948). "Consistent estimates based on partially consistent observations". Econometrica, 16(1), 1-32.

Examples

Run this code

# \donttest{
# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)

# Apply analytical bias correction
mod.bc <- biasCorr(mod)
summary(mod.bc)
# }