EconPapers    
Economics at your fingertips  
 

REDI: Stata module providing a Random Empirical Distribution Imputation method for estimating continuous incomes

Molly King ()
Additional contact information
Molly King: Santa Clara University

Statistical Software Components from Boston College Department of Economics

Abstract: redi is a method for cold-deck imputation of a continuous distribution from binned incomes, using a real-world reference dataset (in this case, the CPS ASEC). The Random Empirical Distribution Imputation (redi) method imputes discrete observations using binned income data. The user may wish to combine or compare income data across years or surveys, stymied by incompatible categories. The redi package converts categorical to continuous incomes through random cold-deck imputation from a real world reference dataset. The redi method reconciles bins between datasets or across years and handles top incomes. redi has other advantages of computing an income distribution that is nonparametric, bin consistent, area- and variance-preserving, and continuous.

Language: Stata
Requires: Stata version 16
Keywords: data management; imputation; income (search for similar items in EconPapers)
Date: 2022-07-13
Note: This module should be installed from within Stata by typing "ssc install redi". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
References: Add references at CitEc
Citations:

Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/r/redi.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/r/redi.sthlp help file (text/plain)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:bocode:s459100

Ordering information: This software item can be ordered from
http://repec.org/docs/ssc.php

Access Statistics for this software item

More software in Statistical Software Components from Boston College Department of Economics Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().

 
Page updated 2024-10-29
Handle: RePEc:boc:bocode:s459100