Today Bo Andrée defended his PhD dissertation in an online session that had a live stream on YouTube. The somewhat impersonal, digital setting did not inhibit a lively discussion.

Bo’s thesis sets out to develop theory and methods to analyze dynamic interactions between observations that are interrelated across space and time. This type of modeling is becoming increasingly important as sensors and institutions continue to gather rich subnational spatial time series of remotely sensed or surveyed economic variables. Going from finance, to macroeconomics or the environment, nearly all policy relevant phenomena in the socio-economic domain involve multivariate interactions across both spatial and temporal dimensions. Analyzing these problems raises a number of inquiries about the methods used that are both practically and theoretically interesting. In particular, cross-sectional data is often spatially dependent. Together with possible endogenous interactions between the observations of the different variables that are collected sequentially over the time dimension, this produces complex feedback properties that may violate various assumptions made by standard models. Second, as the dimensions of datasets grow, it becomes increasingly unlikely that linear relationships provide a realistic description of the phenomena at hand. The tendency of nonlinearities and the complex feedback properties that characterize spatial time series, render many related estimation problems non-standard. The thesis and the papers it contains cab be found here:

Theory and Application of Dynamic Spatial Time Series Models