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Modelling compositional time series from repeated surveys

Record type: Article

A compositional time series is defined as multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. In this case, the survey estimates are proportions of a whole subject to a unitysum constraint. In this paper we employ a state space approach for modelling compositional time series from repeated surveys taking into account the sampling errors. The additive logistic transformation is used in order to guarantee predictions and signal estimates bounded between zero and one which satisfy the unitysum constraint. The method is applied to compositional data from the Brazilian Labour Force Survey. Estimates of the vector of proportions and the unemployment rate are obtained. In addition, the structural components of the signal vector, such as the seasonals and the trends, are produced.

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Citation

Silva, D.B.N. and Smith, T.M.F. (2001) Modelling compositional time series from repeated surveys Survey Methodology, 27, (2), pp. 205-215.

More information

Published date: 2001
Keywords: error analysis, framework, labour force survey, logistic regression analysis, models, sampling and weighting, statistical data, surveys, unemployment rates
Organisations: Statistics

Identifiers

Local EPrints ID: 30037
URI: http://eprints.soton.ac.uk/id/eprint/30037
ISSN: 0714-0045
PURE UUID: 2936a61e-ab5f-4eab-8091-8ea602c4f1d7

Catalogue record

Date deposited: 30 Apr 2007
Last modified: 17 Jul 2017 15:55

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Contributors

Author: D.B.N. Silva
Author: T.M.F. Smith

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