Modelling compositional time series from repeated surveys

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


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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.

Item Type: Article
ISSNs: 0714-0045 (print)
Related URLs:
Keywords: error analysis, framework, labour force survey, logistic regression analysis, models, sampling and weighting, statistical data, surveys, unemployment rates
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 30037
Accepted Date and Publication Date:
Date Deposited: 30 Apr 2007
Last Modified: 31 Mar 2016 11:56

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