fit_and_predict_probabilities

This function fits a binary choice model and predicts probabilities for every
observations.

Implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean.
The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018).
The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates.
References:
Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) <doi:10.3390/econometrics6020028>. "Decomposing Wage Distributions Using Recentered Influence Function Regressions."
Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) <doi:10.3386/w16045>. "Decomposition Methods in Economics."
DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) <doi:10.2307/2171954>. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach."
Oaxaca, Ronald. (1973) <doi:10.2307/2525981>. "Male-Female Wage Differentials in Urban Labor Markets."
Blinder, Alan S. (1973) <doi:10.2307/144855>. "Wage Discrimination: Reduced Form and Structural Estimates."

Samuel Meier

ddecompose

Detailed Distributional Decomposition

fit_and_predict_probabilities function

<dl><dt>formula</dt>
<dd><code>formula</code> object specifying the conditional probability model.</dd>
<dt>data_used</dt>
<dd><code>data.frame</code> with data.</dd>
<dt>weights</dt>
<dd>weights variable</dd>
<dt>method</dt>
<dd>method to estimate conditional probabilities</dd>
<dt>return_model</dt>
<dd>boolean: If <code>FALSE</code> (default), the object of the model
fit used to predict the conditional probabilities for the reweighting factor
are not returned.</dd>
<dt>newdata</dt>
<dd><code>data.frame</code> with data to be used for predictions.</dd>
<dt>...</dt>
<dd>other parameters passed to the estimation function.</dd></dl>

Arguments

Predict conditional probabilities — fit_and_predict_probabilities

<dl>

<dt>formula</dt>
<dd><code>formula</code> object specifying the conditional probability model.</dd>


<dt>data_used</dt>
<dd><code>data.frame</code> with data.</dd>


<dt>weights</dt>
<dd>weights variable</dd>


<dt>method</dt>
<dd>method to estimate conditional probabilities</dd>


<dt>return_model</dt>
<dd>boolean: If <code>FALSE</code> (default), the object of the model
fit used to predict the conditional probabilities for the reweighting factor
are not returned.</dd>


<dt>newdata</dt>
<dd><code>data.frame</code> with data to be used for predictions.</dd>


<dt>...</dt>
<dd>other parameters passed to the estimation function.</dd>

</dl>

fit_and_predict_probabilities: Predict conditional probabilities

Description

Usage

Arguments