biasCorr: Asymptotic bias-correction after fitting binary choice models with two-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 non-linear fixed effects models (see Fernandez-Val and Weidner (2018) for an overview). The command applies the analytical bias-correction derived by Fernandez-Val and Weinder (2016) to obtain bias-corrected estimates of the structural parameters and is currently restricted to logit and probit models.

Usage

biasCorr(object = NULL, L = 0L)

Arguments

object

an object of class "feglm"; currently restricted to binomial with "logit" or "probit" link function.

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. In the presence of weakly exogenous or predetermined regressors, Fernandez-Val and Weidner (2016, 2018) suggest to choose a bandwidth not higher than four.

Value

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

References

Czarnowske, D. and Stammann, A. (2019). "Binary Choice Models with High-Dimensional Individual and Time Fixed Effects". ArXiv e-prints.

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

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

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

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

Examples

Run this code

# NOT RUN {
# 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)
# }