compute_ratio_variance

Function to compute the variance bounds

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

compute_ratio_variance function

<dl><dt>x</dt>
<dd>a matrix containing the directions to compute the variance bounds on the radial function.</dd>
<dt>Xp</dt>
<dd>the observations of the noncommon regressor (possibly conditional on Xc).</dd>
<dt>Yp</dt>
<dd>the observations of the outcome variable.</dd>
<dt>dimX2</dt>
<dd>the dimension of the noncommon regressors Xnc.</dd>
<dt>weights_xp</dt>
<dd>the sampling or bootstrap weights for the dataset (Xnc,Xc).</dd>
<dt>weights_yp</dt>
<dd>the sampling or bootstrap weights for the dataset (Y,Xc).</dd></dl>

Arguments

Function to compute the variance bounds — compute_ratio_variance

<dl>

<dt>x</dt>
<dd>a matrix containing the directions to compute the variance bounds on the radial function.</dd>


<dt>Xp</dt>
<dd>the observations of the noncommon regressor (possibly conditional on Xc).</dd>


<dt>Yp</dt>
<dd>the observations of the outcome variable.</dd>


<dt>dimX2</dt>
<dd>the dimension of the noncommon regressors Xnc.</dd>


<dt>weights_xp</dt>
<dd>the sampling or bootstrap weights for the dataset (Xnc,Xc).</dd>


<dt>weights_yp</dt>
<dd>the sampling or bootstrap weights for the dataset (Y,Xc).</dd>

</dl>

compute_ratio_variance: Function to compute the variance bounds

Description

Usage

Value

Arguments