A Primer for Spatial Econometrics: With Applications in R, STATA and Python, 2nd EditionКНИГИ » ПРОГРАММИНГ
Название: A Primer for Spatial Econometrics: With Applications in R, STATA and Python, 2nd Edition Автор: Giuseppe Arbia Издательство: Palgrave Macmillan Год: 2024 Страниц: 250 Язык: английский Формат: pdf (true) Размер: 13.6 MB
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.
The work of revision in these sections was not limited to an update of the R codes, because I also considered necessary to introduce totally new sections devoted to the STATA software and to the Python programming language. Indeed, a large number of users, especially economists, have started using spatial econometrics techniques in their applied work and showed a clear inclination toward the use of STATA as the software of election. For this reason, I decided to add to the R codes presented in the first edition also tutorials to help practice also on this second software tool. Furthermore, in the last 10 years, the use of Python as a general-purpose programming language has become more and more widespread, paralleling the enormous development of applications of spatial econometric models and methods in many scientific disciplines different from the traditional socio-economic areas, especially in the hard sciences. For this reason, I thought that a modern textbook on spatial econometrics could not be offered to the readers without also including some tutorials to let them apply the techniques presented to their specific field of analysis by using this new emerging language.
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A Primer for Spatial Econometrics: With Applications in R, STATA and Python, 2nd Edition 
