bife (version 0.5)

apeff_bife: Average Partial Effects for Binary Choice Models with Fixed Effects

Description

apeff_bife is a function used to compute average partial effects for fixed effects binary choice models. It is able to compute bias-corrected average partial effects derived by Newey and Hahn (2004) to account for the incidental parameters bias.

Usage

apeff_bife(mod, discrete = NULL, bias_corr = "ana", iter_demeaning = 100,
  tol_demeaning = 1e-05, iter_offset = 1000, tol_offset = 1e-05)

Arguments

mod

an object of class bife.

discrete

a description of the variables that are discrete regressors. For apeff_bife this has to be a character string naming the discrete regressors. Default is NULL (no discrete regressor(s)).

bias_corr

an optional string that specifies the type of the bias correction: semi or analytical. The value should be any of the values "semi" or "ana". Default is "ana" (analytical bias-correction). Details are given under Details.

iter_demeaning

an optional integer value that specifies the maximum number of iterations of the demeaning algorithm. Default is 100. Details are given under Details.

tol_demeaning

an optional number that specifies the tolerance level of the demeaning algorithm. Default is 1e-5. Details are given under Details.

iter_offset

an optional integer value that specifies the maximum number of iterations of the offset algorithm for the computation of bias-adjusted fixed effects. Default is 1000. Details are given under Details.

tol_offset

an optional number that specifies the tolerance level of the offset algorithm for the computation of bias-adjusted fixed effects. Default is 1e-5. Details are given under Details.

Value

An object of apeff_bife returns a named matrix with at least a first column "apeff" containing the uncorrected average partial effects of the structural variables. An optional second column "apeff_corrected" is returned containing the corrected average partial effects of the structural variables.

Details

The semi bias-corrected average partial effects are computed as usual partial effects with the bias-adjusted fixed effects and the bias-corrected structural parameters.

The analytical bias-corrected average partial effects follow Newey and Hahn (2004). For further details consult the description of bife.

Note: Bias-corrected partial effects can be only returned if the object mod returns bias-corrected coefficients, i.e. if a bias-correction has been used in the previous bife command.

References

Hahn, J., and W. Newey (2004). "Jackknife and analytical bias reduction for nonlinear panel models." Econometrica 72(4), 1295-1319.

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.

See Also

bife

Examples

# NOT RUN {
library("bife")

# Load 'psid' dataset
dataset <- psid
head(dataset)

# Fixed effects logit model w/o bias-correction
mod_no <- bife(LFP ~ AGE + I(INCH / 1000) + KID1 + KID2 + KID3 | ID,
 data = dataset, bias_corr = "no")
               
# Compute uncorrected average partial effects for mod_no
# Note: bias_corr does not affect the result
apeff_bife(mod_no, discrete = c("KID1", "KID2", "KID3")) 

# Fixed effects logit model with analytical bias-correction
mod_ana <- bife(LFP ~ AGE + I(INCH / 1000) + KID1 + KID2 + KID3 | ID,
 data = dataset)
               
# Compute semi-corrected average partial effects for mod_ana
apeff_bife(mod_ana, discrete = c("KID1", "KID2", "KID3"),
 bias_corr = "semi")

# Compute analytical bias-corrected average partial effects
# for mod_ana
apeff_bife(mod_ana, discrete = c("KID1", "KID2", "KID3"))
 
# }