The Portuguese National Statistical Institute is responsible for estimating and publishing quarterly labour market figures at national level for both NUTS I and NUTS II regions. Over recent years it has become increasinglyimportant to identify these figures at more disaggregated levels. However, based on the established direct estimation method, it is not possible to produce satisfactorily precise estimates at higher spatial resolutions. From the 4th quarter of 2014 onwards, all the sampling units, namely the residential buildings, of the Portuguese Labour Force Survey (PLFS) were georeferenced. To take full advantage of this information, Pereira et al. (2019) proposed applying a spatial marked point process approach to unemployment estimation, in which the estimation of the unemployment intensity becomes the focal point. There, the sampling units were assumed to be a realization of a spatial point process, specifically a log Gaussian Cox process, with the number of unemployed in each unit being their respective marks. Recently, further information on the geo-referenced locations of all units of the population, namely all residential buildings in the national territory, became available. Consequently, it is no longer necessary to model the spatial configuration of the units of the population. Thus, we propose a new point referenced model for the marks based on the sampled units and extrapolate this to all units of the population. As expected, this extra information, and as a consequence the new model itself, produce estimates with higher precision.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the projects UID/MAT/00006/2019, PTDC/MAT-STA/28649/2017, and the PhD scholarship SFRH/BD/92728/2013 from Fundação para a Ciência e Tecnologia, Portugal. Instituto Nacional de Estatística and Centro de Estatística e Aplicações da Universidade de Lisboa are the reception institutions. This study is the responsibility of the authors and does not reflect the official opinions of Instituto Nacional de Estatistica.