compute.MDiD

Internal function for computing the actual value for MDiD

internal

Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).

Brantly Callaway

Quantile Treatment Effects

compute.MDiD function

<dl><dt>formla</dt>
<dd>The formula y ~ d where y is the outcome and d is the
treatment indicator (d should be binary), d should be equal to one
in all time periods for individuals that are eventually treated</dd>
<dt>xformla</dt>
<dd>A optional one sided formula for additional covariates that
will be adjusted for. E.g ~ age + education. Additional covariates can
also be passed by name using the x paramater.</dd>
<dt>t</dt>
<dd>The 3rd time period in the sample. Treated individuals should
be treated in this time period and untreated individuals should not be
treated. The code attempts to enforce this condition, but it is good
try to handle this outside the panel.qtet method.</dd>
<dt>tmin1</dt>
<dd>The 2nd time period in the sample. This should be a
pre-treatment period for all individuals in the sample.</dd>
<dt>tname</dt>
<dd>The name of the column containing the time periods</dd>
<dt>data</dt>
<dd>A data.frame containing all the variables used</dd>
<dt>idname</dt>
<dd>The individual (cross-sectional unit) id name</dd>
<dt>probs</dt>
<dd>A vector of values between 0 and 1 to compute the QTET at</dd></dl>

Arguments

compute.MDiD — compute.MDiD

<dl>

<dt>formla</dt>
<dd>The formula y ~ d where y is the outcome and d is the
treatment indicator (d should be binary), d should be equal to one
in all time periods for individuals that are eventually treated</dd>


<dt>xformla</dt>
<dd>A optional one sided formula for additional covariates that
will be adjusted for. E.g ~ age + education. Additional covariates can
also be passed by name using the x paramater.</dd>


<dt>t</dt>
<dd>The 3rd time period in the sample. Treated individuals should
be treated in this time period and untreated individuals should not be
treated. The code attempts to enforce this condition, but it is good
try to handle this outside the panel.qtet method.</dd>


<dt>tmin1</dt>
<dd>The 2nd time period in the sample. This should be a
pre-treatment period for all individuals in the sample.</dd>


<dt>tname</dt>
<dd>The name of the column containing the time periods</dd>


<dt>data</dt>
<dd>A data.frame containing all the variables used</dd>


<dt>idname</dt>
<dd>The individual (cross-sectional unit) id name</dd>


<dt>probs</dt>
<dd>A vector of values between 0 and 1 to compute the QTET at</dd>

</dl>

compute.MDiD: compute.MDiD

Description

Usage

Arguments