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The econometric analysis of non-stat...

Beenstock, Michael.

The econometric analysis of non-stationary spatial panel data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The econometric analysis of non-stationary spatial panel databy Michael Beenstock, Daniel Felsenstein.
作者:	Beenstock, Michael.
其他作者:	Felsenstein, Daniel.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	ix, 275 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Time-series analysis.
電子資源:	https://doi.org/10.1007/978-3-030-03614-0
ISBN:	9783030036140$q(electronic bk.)

The econometric analysis of non-stationary spatial panel data
Beenstock, Michael.

The econometric analysis of non-stationary spatial panel data[electronic resource] /by Michael Beenstock, Daniel Felsenstein. - Cham :Springer International Publishing :2019. - ix, 275 p. :ill. (some col.), digital ;24 cm. - Advances in spatial science, the regional science series,1430-9602. - Advances in spatial science, the regional science series..

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.

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 f 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 critical values 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.

ISBN: 9783030036140$q(electronic bk.)

Standard No.: 10.1007/978-3-030-03614-0doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

The econometric analysis of non-stationary spatial panel data
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505 0 $a 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.
520 $a 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 f 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 critical values 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.
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