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

The estimation of gross flows in the presence of measurement error using auxiliary variables
The estimation of gross flows in the presence of measurement error using auxiliary variables
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.
0964-1998
13-32
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Skinner, Chris
412567de-c4cb-4d3d-886a-e17741697022
Humphreys, Keith
4992ab0f-45a9-4944-8703-9e3a974c11f5
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Skinner, Chris
412567de-c4cb-4d3d-886a-e17741697022
Humphreys, Keith
4992ab0f-45a9-4944-8703-9e3a974c11f5

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), 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.

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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

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Date deposited: 11 Feb 2008
Last modified: 15 Mar 2024 07:48

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Contributors

Author: Chris Skinner
Author: Keith Humphreys

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