Survey Methodology
State space time series modelling of the Dutch Labour Force Survey: Model selection and mean squared errors estimation

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by Oksana Bollineni-Balabay, Jan van den Brakel and Franz PalmNote 1

  • Release date: June 22, 2017
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Abstract

Structural time series models are a powerful technique for variance reduction in the framework of small area estimation (SAE) based on repeatedly conducted surveys. Statistics Netherlands implemented a structural time series model to produce monthly figures about the labour force with the Dutch Labour Force Survey (DLFS). Such models, however, contain unknown hyperparameters that have to be estimated before the Kalman filter can be launched to estimate state variables of the model. This paper describes a simulation aimed at studying the properties of hyperparameter estimators in the model. Simulating distributions of the hyperparameter estimators under different model specifications complements standard model diagnostics for state space models. Uncertainty around the model hyperparameters is another major issue. To account for hyperparameter uncertainty in the mean squared errors (MSE) estimates of the DLFS, several estimation approaches known in the literature are considered in a simulation. Apart from the MSE bias comparison, this paper also provides insight into the variances and MSEs of the MSE estimators considered.

Key Words: Bootstrap; Hyperparameter; State space model; True MSE; Unemployment.

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How to cite

Bollineni-Balabay, O., van den Brakel, J. and Palm, F. (2017). State space time series modelling of the Dutch Labour Force Survey: Model selection and mean squared errors estimation. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 43, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2017001/article/14819-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Minister of Industry, 2017

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. The Dutch Labour Force Survey
Section 3. MSE estimation approaches
Section 4. The DLFS-specific simulation setup
Section 5. Results
Section 6. Concluding remarks
Acknowledgements
Appendices
References

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