Theory and Application of Dynamic Spatial Time Series Models

Bo Pieter Johannes Andree

Research output: PhD ThesisPhD Thesis - Research VU Amsterdam, graduation VU Amsterdam

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Abstract

Stochastic economic processes are often characterized by dynamic interactions between variables that are dependent in both space and time. Analyzing these processes raises a number of questions about the econometric methods used that are both practically and theoretically interesting. This work studies econometric approaches to analyze spatial data that evolves dynamically over time. The book provides a background on least squares and maximum likelihood estimators, and discusses some of the limits of basic econometric theory. It then discusses the importance of addressing spatial heterogeneity in policies. The next chapters cover parametric modeling of linear and nonlinear spatial time series, non-parametric modeling of nonlinearities in panel data, modeling of multiple spatial time series variables that exhibit long and short memory, and probabilistic causality in spatial time series settings.
Original languageEnglish
QualificationPhD
Awarding Institution
  • Vrije Universiteit Amsterdam
Supervisors/Advisors
  • Koomen, Eric, Co-supervisor
  • Scholten, Henk, Supervisor
Award date26 May 2020
Publisher
Print ISBNs978903610607
Publication statusPublished - 26 May 2020

Keywords

  • Spatial Time Series
  • Spatial Econometrics
  • Time Series Econometrics
  • Dynamic Panel

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