A dataset with 500 observations of matched parent's family income and child's family income data that also contains information on the education level of the family head (this is primary earner in the family)

datasets

Estimates conditional distributions and conditional quantiles. The versions of the methods in this package are primarily for use in multiple step procedures where the first step is to estimate a conditional distribution. In particular, there are functions for implementing distribution regression, quantile regression, and versions of local linear distribution regression; all in a unified framework. Distribution regression provides a way to flexibly model the distribution of some outcome Y conditional on covariates X without imposing parametric assumptions on the conditional distribution but providing more structure than fully nonparametric estimation (See Foresi and Peracchi (1995) <doi:10.2307/2291056> and Chernozhukov, Fernandez-Val, and Melly (2013) <doi:10.3982/ECTA10582>).

Brantly Callaway

TempleMetrics

Estimating Conditional Distributions

Weige Huang

igm function

A data frame with 500 rows and 3 columns<dl class="dl-horizontal">
 <dt>lcfincome</dt><dd>log of child's family income</dd>
 <dt>lfincome</dt><dd>log of parent's family income</dd>
 <dt>HEDUC</dt><dd>head of family's education; less than HS, HS, or COLLEGE</dd>
</dl>

Format

Intergenerational Mobility data from the PSID — igm

A data frame with 500 rows and 3 columns<dl class='dl-horizontal'>
 <dt>lcfincome</dt><dd>log of child's family income</dd>
 <dt>lfincome</dt><dd>log of parent's family income</dd>
 <dt>HEDUC</dt><dd>head of family's education; less than HS, HS, or COLLEGE</dd>
</dl>

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igm: Intergenerational Mobility data from the PSID

Description

Usage

Arguments

Format