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A Primer for Spatial Econometrics

- With Applications in R and Stata and Python

Om A Primer for Spatial Econometrics

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.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783031571817
  • Bindende:
  • Paperback
  • Utgitt:
  • 31. oktober 2024
  På lager
Leveringstid: 4-8 virkedager
Forventet levering: 3. januar 2025
Utvidet returrett til 31. januar 2025
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Beskrivelse av A Primer for Spatial Econometrics

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.

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