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010   $a3-030-03614-6
024 7 $a10.1007/978-3-030-03614-0
035   $a(CKB)4930000000042010
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035   $a(DE-He213)978-3-030-03614-0
035   $a(EXLCZ)994930000000042010
100   $a20190327d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 14$aThe Econometric Analysis of Non-Stationary Spatial Panel Data /$fby Michael Beenstock, Daniel Felsenstein
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (280 pages)
225 1 $aAdvances in Spatial Science, The Regional Science Series,$x1430-9602
311   $a3-030-03613-8 
320   $aIncludes bibliographical references.
327   $a1 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. .
330   $aThis monograph deals with spatially dependent non-stationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial non-stationarity in spatial cross-section data, and a full exposition of non stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models. The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical values for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent. The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel. .
410  0$aAdvances in Spatial Science, The Regional Science Series,$x1430-9602
606   $aEconometrics
606   $aRegional economics
606   $aSpatial economics
606   $aStatistics 
606   $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010
606   $aRegional/Spatial Science$3https://scigraph.springernature.com/ontologies/product-market-codes/W49000
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
606   $aEconometria$2thub
606   $aAnàlisi de sèries temporals$2thub
608   $aLlibres electrònics$2thub
615  0$aEconometrics.
615  0$aRegional economics.
615  0$aSpatial economics.
615  0$aStatistics .
615 14$aEconometrics.
615 24$aRegional/Spatial Science.
615 24$aStatistics for Business, Management, Economics, Finance, Insurance.
615  7$aEconometria
615  7$aAnàlisi de sèries temporals
676   $a330.015195
676   $a519.55
700   $aBeenstock$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$0122913
702   $aFelsenstein$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910337680903321
996   $aThe Econometric Analysis of Non-Stationary Spatial Panel Data$92232924
997   $aUNINA