bias_corr: Asymptotic bias correction after fitting binary choice models...

In capybara: Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

Asymptotic bias correction after fitting binary choice models with a 1,2,3-way error component

Description

Post-estimation routine to substantially reduce the incidental parameter bias problem. 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 1,2,3-way fixed effects.

Usage

bias_corr(object = NULL, l = 0L, panel_structure = c("classic", "network"))

Arguments

object	an object of class "feglm".
l	unsigned integer indicating a bandwidth for the estimation of spectral densities proposed by Hahn and Kuersteiner (2011). The 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, we 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.
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".

Value

A named list of classes "bias_corr" and "feglm".

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.

See Also

feglm

Examples

# subset trade flows to avoid fitting time warnings during check
set.seed(123)
trade_2006 <- trade_panel[trade_panel$year == 2006, ]
trade_2006 <- trade_2006[sample(nrow(trade_2006), 500), ]

trade_2006$trade <- ifelse(trade_2006$trade > 100, 1L, 0L)

# Fit 'feglm()'
mod <- feglm(trade ~ lang | year, trade_2006, family = binomial())

# Apply analytical bias correction
mod_bc <- bias_corr(mod)
summary(mod_bc)

capybara documentation built on April 11, 2025, 5:41 p.m.