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 language | English |
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Qualification | PhD |
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Award date | 26 May 2020 |
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Print ISBNs | 978903610607 |
Publication status | Published - 26 May 2020 |
Keywords
- Spatial Time Series
- Spatial Econometrics
- Time Series Econometrics
- Dynamic Panel