angrist

A random sample of 1000 observations from the data set
 used by Angrist and Krueger in their investigation of the impact '
 of education on future wages.

datasets

Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial. Individuals are matched to be near on measured confounders and far on levels of an instrumental variable. Methods outlined in further detail in Rigdon, Baiocchi, and Basu (2018) <doi:10.18637/jss.v086.c05>.

Joseph Rigdon

nearfar

Near-Far Matching

angrist function

A data frame with 1000 observations on the following 7 variables.<dl>
 <dt><code>wage</code></dt>
<dd>a numeric vector</dd> <dt><code>educ</code></dt>
<dd>a numeric vector</dd> <dt><code>qob</code></dt>
<dd>a numeric vector</dd> <dt><code>IV</code></dt>
<dd>a numeric vector</dd> <dt><code>age</code></dt>
<dd>a numeric vector</dd> <dt><code>married</code></dt>
<dd>a numeric vector</dd> <dt><code>race</code></dt>
<dd>a numeric vector</dd> 
</dl>

Format

Angrist data set for education and wages — angrist

A data frame with 1000 observations on the following 7 variables.<dl>
 <dt><code>wage</code></dt>
<dd>a numeric vector</dd>

 <dt><code>educ</code></dt>
<dd>a numeric vector</dd>

 <dt><code>qob</code></dt>
<dd>a numeric vector</dd>

 <dt><code>IV</code></dt>
<dd>a numeric vector</dd>

 <dt><code>age</code></dt>
<dd>a numeric vector</dd>

 <dt><code>married</code></dt>
<dd>a numeric vector</dd>

 <dt><code>race</code></dt>
<dd>a numeric vector</dd>

 
</dl>

angrist: Angrist data set for education and wages

Description

Arguments

Format

Details

References

Examples