get_APEs {bife}  R Documentation 
get_APEs
is a postestimation routine that can be used to estimate average partial
effects with respect to all covariates in the model and the corresponding covariance matrix. The
estimation of the covariance is based on a linear approximation (delta method). Note that
the command automatically determines which of the regressors are continuous or binary.
Remark: The routine currently does not allow to compute average partial effects based on functional forms like interactions and polynomials.
Note: apeff_bife
is deprecated and will be removed soon.
get_APEs(
object,
n_pop = NULL,
sampling_fe = c("independence", "unrestricted"),
weak_exo = FALSE
)
apeff_bife(...)
object 
an object of class 
n_pop 
unsigned integer indicating a finite population correction for the estimation of the
covariance matrix of the average partial effects proposed by
CruzGonzalez, FernándezVal, and Weidner (2017). The correction factor is computed as follows:

sampling_fe 
a string equal to 
weak_exo 
logical indicating if some of the regressors are assumed to be weakly exogenous (e.g.
predetermined). If object is returned by 
... 
arguments passed to the deprecated function 
The function get_APEs
returns a named list of class "bifeAPEs"
.
CruzGonzalez, M., I. FernándezVal, and M. Weidner. (2017). "Bias corrections for probit and logit models with twoway fixed effects". The Stata Journal, 17(3), 517545.
FernándezVal, I. (2009). "Fixed effects estimation of structural parameters and marginal effects in panel probit models". Journal of Econometrics 150(1), 7185.
FernándezVal, I. and M. Weidner (2018). "Fixed effects estimation of larget panel data models". Annual Review of Economics, 10, 109138.
Neyman, J. and E. L. Scott (1948). "Consistent estimates based on partially consistent observations". Econometrica, 16(1), 132.
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)
# Compute average partial effects
mod_ape < get_APEs(mod)
summary(mod_ape)
# Apply analytical bias correction
mod_bc < bias_corr(mod)
summary(mod_bc)
# Compute biascorrected average partial effects
mod_ape_bc < get_APEs(mod_bc)
summary(mod_ape_bc)