Overview

Nonlinear Time Series Econometrics in R: Thresholds, Smooth Transitions, and Networks is a comprehensive guide to advanced methods in time series econometrics, focusing on models that capture asymmetry, regime changes, and nonlinear dynamics often missed by classical linear approaches. Written in a clear, structured, and research-oriented style, this book introduces readers to threshold models, smooth transition autoregressions, nonlinear volatility processes, copula-based dependence structures, and network approaches to interconnected time series. Each concept is paired with rigorous econometric theory, practical intuition, and hands-on implementation using R. Key features of this book include: Comprehensive coverage of nonlinear models: Threshold autoregression (TAR), Smooth Transition Autoregression (STAR), GARCH-family models, copulas, and network methods. Mathematical foundations: Stationarity conditions, asymptotics, and proofs supporting estimation techniques. Hands-on R applications: Step-by-step coding examples with tsDyn, rugarch, copula, and igraph. Advanced econometric tools: Nonlinear dependence, regime-switching, and volatility clustering. Bridging theory and practice: From foundational models to applications in finance, macroeconomics, and systemic risk. This book is designed for graduate students, academic researchers, and practitioners in econometrics, finance, and applied statistics who seek to understand and apply nonlinear methods to real-world time series data. Whether you are investigating exchange rate dynamics, modeling stock market volatility, or studying interconnected macroeconomic indicators, this book equips you with both the theory and the tools needed to advance your empirical analysis.

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