wquant

Smallest <code>y</code> with weighted CDF <code>&gt;= p</code>. Uses a small
numerical tolerance on the comparison so that a <code>p</code> landing exactly
on a floating-point rounding boundary (common with uniform weights, e.g.
<code>1/n</code>) picks the intended order statistic instead of flipping based
on last-bit differences in summation across platforms.

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 covariates (selection on
observables) using the methods of Firpo (2007)
<doi:10.1111/j.1468-0262.2007.00738.x>, and (iii) identification is based
on a Difference in Differences assumption, with support for several
varieties including Athey and Imbens (2006)
<doi:10.1111/j.1468-0262.2006.00668.x>, Callaway and Li (2019)
<doi:10.3982/QE935>, and Callaway, Li, and Oka (2018)
<doi:10.1016/j.jeconom.2018.06.008>. Version 2.0 adds a unified staggered
treatment adoption API (built on 'ptetools') for all DiD-based estimators,
as well as a new lagged-outcome unconfoundedness estimator ('lou_qtt').

Brantly Callaway

Quantile Treatment Effects

wquant function

<dl><dt>y_vec</dt>
<dd>numeric vector of values.</dd>
<dt>w_vec</dt>
<dd>numeric vector of weights, same length as <code>y_vec</code>.</dd>
<dt>probs</dt>
<dd>numeric vector of probabilities in <code>[0, 1]</code>.</dd></dl>

Arguments

Weighted type-1 quantile — wquant

<dl>

<dt>y_vec</dt>
<dd>numeric vector of values.</dd>


<dt>w_vec</dt>
<dd>numeric vector of weights, same length as <code>y_vec</code>.</dd>


<dt>probs</dt>
<dd>numeric vector of probabilities in <code>[0, 1]</code>.</dd>

</dl>

wquant: Weighted type-1 quantile

Description

Usage

Value

Arguments