Analysis of a cross-section of time series using structural time series models
This study deals with multivariate structural time series models, and in particular, with the analysis and modelling of cross-sections of time series. In this context, no cause and effect relationships are assumed between the time series, although they are subject to the same overall environment. The main motivations in the analysis of cross-sections of time series are (i) the gains in efficiency in the estimation of the irregular, trend and seasonal components; and (ii) the analysis of models with common effects. The study contains essentially two parts. The first one considers models with a general specification for the correlation of the irregular, trend and seasonal components across the time series. Four structural time series models are presented, and the estimation of the components of the time series, as well as the estimation of the parameters which define this components, is discussed. The second part of the study deals with dynamic error components models where the irregular, trend and seasonal components are generated by common, as well as individual, effects. The extension to models for multivariate observations of cross-sections is also considered. Several applications of the methods studied are presented. Particularly relevant is an econometric study of the demand for energy in the U. K.