Modeling Compositional Time Series from the Brazilian Labour Force Survey

• Activity Number: 615
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Denise Silva*+ and Eduardo Santiago Rosseti
• Companies: ENCE and ENCE
• Keywords: Compositional data ; Repeated surveys ; Sampling error ; State-Space model

Abstract

A compositional time series is a 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. This paper presents the state-space approach for modelling compositional time series from the Brazilian Labour Force Survey (BLFS) taking into account the sampling errors. The BLFS is a rotating panel survey in which the rotation pattern applies to panels of households. Within each rotation group, a panel of households stays in the sample for four successive months, is rotated out for the following 8 months and is sampled again for another four successive months. The survey collects monthly information about employment according to the International Labour Organization (ILO) definitions. The modelling procedure produces estimates for the vector of employed, unemployed and not in the labour force and also for the unemployment rate series with corresponding estimates for seasonals and trends. The model provides bounded predictions and estimates satisfying the unity-sum constraint while taking into account the sampling errors and the correlation structured implied by the survey rotation pattern.