weighted_quantile

function to recover quantiles of a vector with weights

These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.  Additional utilities support staggered treatment adoption settings, including functions for identifying treatment groups, recovering pre-treatment outcomes and covariate averages, and computing lagged outcomes and first differences in panel data.

Brantly Callaway

BMisc

Miscellaneous Functions for Panel Data, Quantiles, and Printing
Results

weighted_quantile function

<dl><dt>tau</dt>
<dd>a vector of values between 0 and 1</dd>
<dt>cvec</dt>
<dd>a vector to compute quantiles for</dd>
<dt>weights</dt>
<dd>the weights, weighted.checkfun normalizes the weights
to sum to 1.</dd>
<dt>norm</dt>
<dd>normalize the weights so that they have mean of 1, default is
to normalize</dd></dl>

Arguments

weighted_quantile — weighted_quantile

<dl>

<dt>tau</dt>
<dd>a vector of values between 0 and 1</dd>


<dt>cvec</dt>
<dd>a vector to compute quantiles for</dd>


<dt>weights</dt>
<dd>the weights, weighted.checkfun normalizes the weights
to sum to 1.</dd>


<dt>norm</dt>
<dd>normalize the weights so that they have mean of 1, default is
to normalize</dd>

</dl>

weighted_quantile: weighted_quantile

Description

Usage

Value

Arguments

Examples