A Practical Guide to Static and Dynamic Econometric Modelling
Description
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.
More details
Other editions
Additional editions
Person
Dr. Sarit Maitra is a seasoned business leader and academic with over 25 years of experience in the industry and academia, specializing in business analytics, data science, and information technology. With a PhD and MS in Information Technology from Universiti Teknologi PETRONAS, Malaysia, he has held pivotal roles in various global organizations. Currently, as a Professor at Alliance University in Bengaluru, India, Dr. Maitra leverages his extensive industry experience to deliver courses in operations analytics, business analytics, and Big data Analytics. He is passionate about data-driven business, and his research spans business analytics (simulation, stochastic modeling), econometric modeling, predictive modeling, and optimization techniques.
Content
Introduction to Econometrics and Linear Regression.- Hypothesis(es) testing.- Dynamic modelling in Econometrics Foundational knowledge.- Theoretical overview: Capital Asset Pricing and Arbitrage Pricing Theory.- Model implementation and testing.- January effect.- Key takeaways.
