Maitra Sarit (27 results)

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data.Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts 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 in econometrics can grasp and apply the material effectively.The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.

Buch. Condition: Neu. 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.

Hardcover. Condition: Brand New. 214 pages. 9.25x6.10x9.21 inches. In Stock.

Hardcover. Condition: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Condition: new. Questo è un articolo print on demand.

Condition: new. Questo è un articolo print on demand.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 203 pp. Englisch.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data.Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts 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 in econometrics can grasp and apply the material effectively.The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis. 188 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 216 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data.Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts 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 in econometrics can grasp and apply the material effectively.The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch.

Buch. Condition: Neu. Non-Linearity in Econometric Modeling, Vol. 1 | A Practical Approach | Sarit Maitra | Buch | xix | Englisch | 2026 | Springer | EAN 9783032064615 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Hardcover. Condition: new. Hardcover. 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. mso-ansi-language: EN-US;">This book provides a comprehensive guide to econometric modeling, combining theory with practical implementation using Python. Readers will explore linear regression, data transformations, and hypothesis testing, along with advanced topics like the Capital Asset Pricing Model and dynamic modeling techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 216 pp. Englisch.