A Practical Guide to Static and Dynamic Econometric Modelling
Examples and Analysis with Python Code Embedded
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Beschreibung
This book provides a comprehensive guide to econometric modeling, combining theory with practical implementation using Python. It covers key econometric concepts, from data collection and model specification to estimation, inference, and prediction. Readers will explore linear regression, data transformations, and hypothesis testing, along with advanced topics like the Capital Asset Pricing Model and dynamic modeling techniques. With Python code examples, this book bridges theory and practice, making it an essential resource for students, finance professionals, economists, and data scientists seeking to apply econometrics in real-world scenarios.
This book provides a comprehensive guide to econometric modeling, combining theory with practical implementation using Python. It covers key econometric concepts, from data collection and model specification to estimation, inference, and prediction. Readers will explore linear regression, data transformations, and hypothesis testing, along with advanced topics like the Capital Asset Pricing Model and dynamic modeling techniques. With Python code examples, this book bridges theory and practice, making it an essential resource for students, finance professionals, economists, and data scientists seeking to apply econometrics in real-world scenarios.
Provides a comprehensive exploration of econometric modelling using Python code Combines theoretical rigour with practical implementation, detailed diagnostic testing and dynamic modelling Serves as a valuable resource for finance professionals, economists, and data scientists
Autor*in
Sarit Maitra
Themen in »A Practical Guide to Static and Dynamic Econometric Modelling«
Econometrics AutoRegressive Distributed Lag Diagnostic testing Dynamic Modeling Econometric Modeling Measurement error Python for Econometrics Quantitative Finance