Non-Linearity in Econometric Modeling, Vol. 2 : Empirical Applications and Source Code

Sarit Maitra

ISBN 10: 303216303X / ISBN 13: 9783032163035

Published by Springer, Berlin, Springer Nature Switzerland, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Nonlinear models have become indispensable in modern finance and economics, yet their reliance on numerical root-finding methods introduces layers of complexity that demand rigorous attention. This second volume of the two-part series offers a comprehensive and accessible guide to tackling these challenges and applying advanced econometric techniques to real-world financial and economic time series data.Designed for students, professionals, and researchers with a solid foundation in statistics, econometrics, and finance, this book bridges the gap between theory and practice. Concepts are introduced progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience can grasp and apply the material effectively.Key Topics Include:Fundamentals of Non-Linear DynamicsEndogeneity in Econometric ModelsAsymmetric PricingPhysics-Inspired Gravity Models in EconomicsArtificial Intelligence and Machine Learning for Fraud AnalyticsWith practical examples, source code, and interdisciplinary insights, this volume empowers readers to navigate the complexities of nonlinear econometric modeling and apply cutting-edge techniques to contemporary challenges in finance and trade.

Seller Inventory # 9783032163035

Bibliographic Details

• Title: Non-Linearity in Econometric Modeling, Vol. 2 : Empirical Applications and Source Code
• Publisher: Springer, Berlin, Springer Nature Switzerland
• Publication Date: 2026
• Language: English
• ISBN 10: 303216303X
• ISBN 13: 9783032163035
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 155H x 15D millimeters 235 millimeters width by 155 millimeters height by 15 millimeters depth
• Weight: 449 grams

About this title

Synopsis

Nonlinear models have become indispensable in modern finance and economics, yet their reliance on numerical root-finding methods introduces layers of complexity that demand rigorous attention. This second volume of the two-part series offers a comprehensive and accessible guide to tackling these challenges and applying advanced econometric techniques to real-world financial and economic time series data.

Designed for students, professionals, and researchers with a solid foundation in statistics, econometrics, and finance, this book bridges the gap between theory and practice. Concepts are introduced progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience can grasp and apply the material effectively.

Key Topics Include:

• Fundamentals of Non-Linear Dynamics
• Endogeneity in Econometric Models
• Asymmetric Pricing
• Physics-Inspired Gravity Models in Economics
• Artificial Intelligence and Machine Learning for Fraud Analytics

With practical examples, source code, and interdisciplinary insights, this volume empowers readers to navigate the complexities of nonlinear econometric modeling and apply cutting-edge techniques to contemporary challenges in finance and trade.

About the Author

Sarit Maitra received his Ph.D. in information technology from Universiti Teknologi PETRONAS, Malaysia. He is currently affiliated with Alliance School of Business, Alliance University, Bengaluru, India, as Professor, Business Analytics. He comes with nearly three decades of industry experience, specialized in data/big data and business analytics. With deep expertise in data strategy and decision science, he leverages both linear and nonlinear modeling approaches to power simulation, optimization, and decision-support systems consistently translating complex data into measurable business outcomes. He leverages his industry to transform data into actionable insights, lead high-performing teams, and align analytics initiatives with organizational goals. He has contributed to several scholarly works and publications in leading academic journals. He plays a key role in multiple consulting engagements, spearheading analytics strategy and data-driven business decisions to deliver business strategy and success.