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Titlebook: New Directions in Spatial Econometrics; Luc Anselin,Raymond J. G. M. Florax Book 1995 Springer-Verlag Berlin Heidelberg 1995 Estimator.Geo

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发表于 2025-3-21 16:16:12 | 显示全部楼层 |阅读模式
书目名称New Directions in Spatial Econometrics
编辑Luc Anselin,Raymond J. G. M. Florax
视频video
丛书名称Advances in Spatial Science
图书封面Titlebook: New Directions in Spatial Econometrics;  Luc Anselin,Raymond J. G. M. Florax Book 1995 Springer-Verlag Berlin Heidelberg 1995 Estimator.Geo
描述The field of spatial econometrics, which is concerned with statistical and econo­ metric techniques to be used to handle spatial effects in multiregional models, was first touched upon in the 1950s. It was given its name in the early 70s by Jean Paelinck and has expanded since. Its development can be monitored in various monographs that have been published since, starting with the seminal work by Andrew Cliff and Keith Ord. Also, the wide array of journals in which contributions to spatial econometrics have been published, shows that the relevance of the field is not restricted to regional science, but extends to geography, spatial statistics, biology, psychology, political science and other social sciences. This volume contains a collection of papers that were presented at special sessions on spatial econometrics organized in the context of the European and North American conferences of the Regional Science Association International, that took place in Louvain la Neuve (August 25-28,1992) and in Houston (November 11-14, 1993), respectively. Apart from these conference papers some contributions were written especially for this volume. The central idea of this book is to communicate
出版日期Book 1995
关键词Estimator; Geographic Information Systems; Geographische Informationssysteme; Import; Regression analysi
版次1
doihttps://doi.org/10.1007/978-3-642-79877-1
isbn_softcover978-3-642-79879-5
isbn_ebook978-3-642-79877-1Series ISSN 1430-9602 Series E-ISSN 2197-9375
issn_series 1430-9602
copyrightSpringer-Verlag Berlin Heidelberg 1995
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Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results to the residuals of regression models for cross-sectional data. To date, Moran’s.statistic is still the most widely applied diagnostic for spatial dependence in regression models [e.g., Johnston (1984), King (1987), Case (1991)]. However, in spite of the well known consequences of ignoring spatial
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Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequalityte a technique for changing one to the other. In that paper, the transformation is a multi-step procedure based on Ripley’s second order statistic (1981). In this chapter, I will briefly review the argument for the filtering procedure and propose a simplified method based on a spatial statistic deve
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Spatial Effects in Probit Models: A Monte Carlo Investigationptions fail: ordinary least squares (OLS) estimates remain consistent if errors are not homoscedastic or are autocorrelated. Estimators for models with discrete data are not always as forgiving as OLS. For example, the Standard probit estimator continues to provide consistent estimates when error te
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Estimating Logit Models with Spatial Dependence), Cressie (1991)]. However, relatively little work has been done on incorporating spatial dependencies into models with qualitative dependent variables. Boots and Kanaroglou (1988) have incorporated spatial considerations into a migration model and Anne Case (1992) has done likewise for a model of
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