get_APEs: Compute average partial effects for binary choice models with fixed effects

Description

get_APEs is a post-estimation 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.

Usage

get_APEs(
  object,
  n_pop = NULL,
  sampling_fe = c("independence", "unrestricted"),
  weak_exo = FALSE
)
apeff_bife(...)

Value

The function get_APEs returns a named list of class "bifeAPEs".

Arguments

object: an object of class "bife".
n_pop: unsigned integer indicating a finite population correction for the estimation of the covariance matrix of the average partial effects proposed by Cruz-Gonzalez, Fernández-Val, and Weidner (2017). The correction factor is computed as follows: \((n^{\ast} - n) / (n^{\ast} - 1)\), where \(n^{\ast}\) and \(n\) are the size of the entire population and the full sample size. Default is NULL, which refers to a factor of zero and a covariance obtained by the delta method.
sampling_fe: a string equal to "independence" or "unrestricted" which imposes sampling assumptions about the unobserved effects. "independence" imposes that all unobserved effects are mutually independent sequences. "unrestricted" does not impose any sampling assumptions. Note that this option only affects the optional finite population correction. Default is "independence".
weak_exo: logical indicating if some of the regressors are assumed to be weakly exogenous (e.g. predetermined). If object is returned by bias_corr, the option will be automatically set to TRUE if the chosen bandwidth parameter is larger than zero. Note that this option only affects the estimation of the covariance matrix. Default is FALSE, which assumes that all regressors are strictly exogenous.
...: arguments passed to the deprecated function apeff_bife.

References

Cruz-Gonzalez, M., I. Fernández-Val, and M. Weidner. (2017). "Bias corrections for probit and logit models with two-way fixed effects". The Stata Journal, 17(3), 517-545.

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.

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.

Examples

Run this code

# \donttest{
# 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 bias-corrected average partial effects
mod_ape_bc <- get_APEs(mod_bc)
summary(mod_ape_bc)
# }