State space methods for official statistics and climate modelling

Cover, Multivariate State Space Methods for Official Statstics and Climate Modelling, Caterina Schiavoni
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Multivariate state space methods for estimating official statistics and modelling climate data.

In this thesis, multivariate state space models are developed to combine time series observed with repeated sample surveys with a large number of auxiliary series based on related Google searches. To avoid high-dimensionality problems, a dynamic factor model is developed.

Furthermore, a method to allow for time-varying correlations between trends in a seemingly unrelated time series equation model is proposed. Finally, spatial state space models are developed for modelling nitrogen concentrations in the atmosphere.

Schiavoni, C (1992). Multivariate state space methods for official statistics and climate modelling. Dissertation, Maastricht University, doi:10.26481/dis.20211104cs.