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Titlebook: A Primer for Spatial Econometrics; With Applications in Giuseppe Arbia Textbook 2024Latest edition The Editor(s) (if applicable) and The Au

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发表于 2025-3-21 16:55:27 | 显示全部楼层 |阅读模式
期刊全称A Primer for Spatial Econometrics
期刊简称With Applications in
影响因子2023Giuseppe Arbia
视频video
发行地址Includes new methodological developments and software routines in R that have emerged in the last 5 years.Extends the practical applications to the use of STATA.Includes new appendices focused on STAT
学科分类Palgrave Texts in Econometrics
图书封面Titlebook: A Primer for Spatial Econometrics; With Applications in Giuseppe Arbia Textbook 2024Latest edition The Editor(s) (if applicable) and The Au
影响因子.This textbook offers a practical and engaging introduction to spatial econometric modelling, detailing the key models, methodologies and tools required to successfully apply a spatial approach...The second edition contains new methodological developments, new references and new software routines in R that have emerged since the first edition published in 2014. It also extends practical applications with the use of the software STATA and of the programming language Python. The first software is used increasingly by many economists, applied econometricians and social scientists while the software Python is becoming the elective choice in many scientific applications. With new statistical appendices in R, STATA and Python, as well as worked examples, learning questions, exercises and technical definitions, this is a significantly expanded second edition that will be a valuable resource for advanced students of econometrics..
Pindex Textbook 2024Latest edition
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On Information Analysis in Phytosociologytes an extension of the popular definition of time lag. Based on thee definitions, we then present a test of the spatial autocorrelation among the regression residuals. The chapter ends with the computer codes needed to implement the procedure discussed in the computer  language R, STATA and Python.
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https://doi.org/10.1007/978-94-015-7241-5s the implications of treating non-deterministic weight matrices. Finally, consistently with the rest of the book, the chapter offers a detailed presentation of the computer codes in R, STATA and Python, needed for the practical implementation of all the models presented.
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The Classical Linear Regression Model,situations. Finally, the chapter contains a presentation of the codes necessary for the practical implementation of the procedures discussed in the chapter, by using the softwares R, STATA and Python.
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Some Important Spatial Definitions,tes an extension of the popular definition of time lag. Based on thee definitions, we then present a test of the spatial autocorrelation among the regression residuals. The chapter ends with the computer codes needed to implement the procedure discussed in the computer  language R, STATA and Python.
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