get_APEs {bife} R Documentation

## 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(...)
```

### 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.pop - n) / (n.pop - 1), where n.pop and n are the size of the entire population and the full sample size. Default is `NULL`, which refers to a factor of one and is equal to an infinitely large population. `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 estimation of the covariance. 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 choosen 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`.

### Value

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

### 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.

### See Also

`bias_corr`, `bife`

### Examples

```
# 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)

```

[Package bife version 0.7.1 Index]