UZH-Logo

Maintenance Infos

A Markov chain Monte Carlo algorithm for multiple imputation in large surveys


Schunk, D (2008). A Markov chain Monte Carlo algorithm for multiple imputation in large surveys. Advances in Statistical Analysis (AStA), 92(1):101-114.

Abstract

Important empirical information on household behavior and household finances, used heavily by researchers, central banks, and for policy consulting, is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. All the more, it is important to explore techniques for the imputation of large survey data. This paper presents the theoretical underpinnings of a Markov Chain Monte Carlo multiple imputation procedure and outlines important technical aspects of the application of MCMC-type algorithms to large socio-economic datasets. In an exemplary application it is found that MCMC algorithms
have good convergence properties even on large datasets with complex patterns of missingness, and that the use of a rich set of covariates in the imputation models has a
substantial effect on the distributions of key financial variables.

Important empirical information on household behavior and household finances, used heavily by researchers, central banks, and for policy consulting, is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. All the more, it is important to explore techniques for the imputation of large survey data. This paper presents the theoretical underpinnings of a Markov Chain Monte Carlo multiple imputation procedure and outlines important technical aspects of the application of MCMC-type algorithms to large socio-economic datasets. In an exemplary application it is found that MCMC algorithms
have good convergence properties even on large datasets with complex patterns of missingness, and that the use of a rich set of covariates in the imputation models has a
substantial effect on the distributions of key financial variables.

Citations

21 citations in Web of Science®
22 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

135 downloads since deposited on 01 Dec 2008
33 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:February 2008
Deposited On:01 Dec 2008 16:04
Last Modified:05 Apr 2016 12:36
Publisher:Springer
ISSN:1863-8171
Additional Information:The original publication is available at www.springerlink.com
Publisher DOI:10.1007/s10182-008-0053-6
Permanent URL: http://doi.org/10.5167/uzh-6194

Download

[img]
Preview
Content: Accepted Version
Filetype: PDF
Size: 1MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations