Modelling error dependence in categorical longitudinal data

Dimitris Pavlopoulos, Paulina Pankowska, Bart Bakker, Daniel Oberski

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Hidden Markov models (HMMs) offer an attractive way of accounting and correcting for measurement error in longitudinal data as they do not require the use of a ‘gold standard’ data source as a benchmark. However, while the standard HMM assumes the errors to be independent or random, some common situations in survey and register data cause measurement error to be systematic. HMMs can correct for systematic error as well if the local independence assumption is relaxed. In this chapter, we present several (mixed) HMMs that relax this assumption with the use of two independent indicators for the variable of interest. Finally, we illustrate the results of some of these HMMs with the use of an example of employment mobility. For this purpose, we use linked survey-register data from the Netherlands.

Original languageEnglish
Title of host publicationMeasurement Error in Longitudinal Data
EditorsAlexandru Cernat, Joseph W. Sakshaug
PublisherOxford University Press
Chapter8
Pages173-194
Number of pages22
ISBN (Electronic)9780198859987
DOIs
Publication statusPublished - 20 May 2021

Keywords

  • Hidden markov model
  • Local independence
  • Measurement error

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