Inference on Spatial-Temporal Econometric Models

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Much economic data are recorded over both space and time. Frequently they exhibit correlation, as well as trending behaviour. A suitable econometric model enables analysis and estimation of such phenomena. In particular, correcting for spatial and temporal correlation can lead to improved estimation of parameters of economic interest and tests of economic hypotheses, with implications for economic policy. We make use of possible knowledge of spatial locations or of economic distances between locations, or estimates of such distances, or else of a large number of temporal observations, to estimate spatial correlation, as well as employing knowledge of regularity or irregularity of temporal observations to estimate time series correlation. A broad and flexible model class will be considered, allowing for a continuum of strong to weak correlation, as well as general forms of trending behaviour, and model components whose functional form is unknown, with a view to reducing risk of misspecification. Methods of estimation and testing that are relatively reliable, and precise, will be proposed, and theory developed that rigorously justifies decision-making rules. This theory will be supplemented by simulation studies and applications to empirical data, and software embodying the econometric procedures will be written and made freely available to practitioners.