bias_corr {bife} | R Documentation |
bias_corr
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 Fernández-Val and Weidner (2018) for an overview). The command applies the analytical
bias correction derived by Fernández-Val (2009) to obtain bias-corrected estimates of
the structural parameters.
Remark: Fernández-Val (2009) further refined the bias correction of Hahn and Newey (2004). The correction is now also applicable to models with weakly exogenous regressors.
bias_corr(object, L = 0L)
object |
an object of class |
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. In the presence of weakly exogenous or predetermined regressors, Fernández-Val and Weidner (2018) suggest to choose a bandwidth not higher than four. |
The function bias_corr
returns a named list of class "bife"
.
Fernández-Val, I. (2009). "Fixed effects estimation of structural parameters and marginal effects in panel probit models". Journal of Econometrics 150(1), 71-85.
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.
Hahn, J. and W. Newey (2004). "Jackknife and analytical bias reduction for nonlinear panel models". Econometrica 72(4), 1295-1319.
Neyman, J. and E. L. Scott (1948). "Consistent estimates based on partially consistent observations". Econometrica, 16(1), 1-32.
Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.
# Load 'psid' dataset
library(bife)
dataset <- psid
# Fit a static logit model
mod <- bife(LFP ~ I(AGE^2) + log(INCH) + KID1 + KID2 + KID3 + factor(TIME) | ID, dataset)
summary(mod)
# Apply analytical bias correction
mod_bc <- bias_corr(mod)
summary(mod_bc)