ewcdf

Compute the weighted empirical cumulative distribution

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Christophe Gaillac

RegCombin

Partially Linear Regression under Data Combination

Xavier D'Haultfoeuille

Arnaud Maurel

ewcdf function

<dl><dt>x</dt>
<dd>the sample</dd>
<dt>weights</dt>
<dd>the associated weights if any. Default is uniform.</dd></dl>

Arguments

Compute the weighted empirical cumulative distribution — ewcdf

<dl>

<dt>x</dt>
<dd>the sample</dd>


<dt>weights</dt>
<dd>the associated weights if any. Default is uniform.</dd>

</dl>

ewcdf: Compute the weighted empirical cumulative distribution

Description

Usage

Value

Arguments