The Econometric Analysis of Non-Stationary Spatial Panel Data

This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial nonstationarity in spatial cross-section data, and a full exposition of non-stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models.
The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical valuesfor panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent.
The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel.

Gives specific focus to the econometrics of nonstationary spatial panel data Provides numerous worked empirical examples for the methodologies presented Provides new critical values for panel unit root tests and panel cointegration tests when the data are spatially dependent

Auteur
Michael Beenstock is the Pinchas Sapir Professor of Economics at the Hebrew University of Jerusalem. He received his Ph.D. from the London School of Economics and Political Science, and was a professor at City University, London from 1981-87. He is a Fellow of the Centre for Economic Policy Research, London. His research interests include applied econometrics, spatial econometrics, world economics, the Israeli economy, energy economics, immigrant absorption, hosung markets and economic inequality. Daniel Felsenstein is a Professor and Chair of the Department of Geography at the Hebrew University of Jerusalem. His fields of interest are economic geography, regional science and spatial econometrics. His research relates to regional disparities, landuse-transportation modeling, urban resilience and local housing markets..

Texte du rabat

This monograph deals with spatially dependent non-stationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial non-stationarity in spatial cross-section data, and a full exposition of non stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models.
The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time criticalvalues for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent.
The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel.

Contenu
1 Space and Time are Inextricably Interwoven.- 2 Time Series for Spatial Econometricians.- 3 Spatial Data Analysis and Econometrics.- 4 The Spatial Conectivity Matrix.- 5 Unit Root and Cointegration Tests in Spatial Cross-Section Data.- 6 Spatial Vector Autoregressions.- 7 Unit Root and Cointegration Tests for Spatially Dependent Panel Data.- 8 Cointegration in Non-Stationary Panel Data.- 9 Spatial Vector Error Correction.- 10 Strong and Weak Cross-Section Dependence in Non-Stationary Spatial Panel Data.