The University of Southampton
University of Southampton Institutional Repository

The estimation of gross flows in the presence of measurement error using auxiliary variables

Pfeffermann, Danny, Skinner, Chris and Humphreys, Keith (1998) The estimation of gross flows in the presence of measurement error using auxiliary variables Journal of the Royal Statistical Society: Series A (Statistics in Society), 161, (1), pp. 13-32. (doi:10.1111/1467-985X.00088).

Record type: Article

Abstract

Classification error can lead to substantial biases in the estimation of gross flows from longitudinal data. We propose a method to adjust flow estimates for bias, based on fitting separate multinomial logistic models to the classification error probabilities and the true state transition probabilities using values of auxiliary variables. Our approach has the advantages that it does not require external information on misclassification rates, it permits the identification of factors that are related to misclassification and true transitions and it does not assume independence between classification errors at successive points in time. Constraining the prediction of the stocks to agree with the observed stocks protects against model misspecification. We apply the approach to data on women from the Panel Study of Income Dynamics with three categories of labour force status. The model fitted is shown to have interpretable coefficient estimates and to provide a good fit. Simulation results indicate good performance of the model in predicting the true flows and robustness against departures from the model postulated.

Full text not available from this repository.

More information

Published date: 1998

Identifiers

Local EPrints ID: 34674
URI: http://eprints.soton.ac.uk/id/eprint/34674
ISSN: 0964-1998
PURE UUID: 6f322efc-da17-4a37-9888-cebcf669400d

Catalogue record

Date deposited: 11 Feb 2008
Last modified: 17 Jul 2017 15:49

Export record

Altmetrics

Contributors

Author: Chris Skinner
Author: Keith Humphreys

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×