Focusing on methods for data that are ordered in time, this textbook provides a comprehensive guide to analyzing time series data using modern techniques from data science. It is specifically tailored to economics and finance applications, aiming to provide students with rigorous training. Chapters cover Bayesian approaches, nonparametric smoothing methods, machine learning, and continuous time econometrics. Theoretical and empirical exercises, concise summaries, bolded key terms, and illustrative examples are included throughout to reinforce key concepts and bolster understanding. Ancillary materials include an instructor's manual with solutions and additional exercises, PowerPoint lecture slides, and datasets. With its clear and accessible style, this textbook is an essential tool for advanced undergraduate and graduate students in economics, finance, and statistics.

‘This is an excellent textbook for advanced undergraduate and graduate students in economics, finance, and statistics. It contains an up-to-date account of recent developments in time series econometrics, including an insightful presentation of machine learning, smoothing, and other nonparametric approaches, and methods for continuous-time models. The book is largely self-contained, comprehensive, and manages to present complex topics clearly and succinctly. It will be very useful both to readers who have not been previously exposed to time series and to those who wish to learn how the more advanced methods work and how they compare and interface with the classical methods.’

Yacine Aït-Sahalia - Princeton University

‘This new time series textbook by Professor Oliver Linton represents a significant contribution to the field, blending rigorous theoretical insights with practical applications in a way that is both accessible and profound. Linton is a world-leading scholar on economic and financial time series, and his meticulous attention to detail and his ability to translate complex concepts into understandable narratives make this book an indispensable resource for students, scholars, and practitioners. It is a must-read for anyone looking to deepen their understanding of modern economic and financial time series. In fact, I have been using draft versions of this book to teach my advanced undergraduate Financial Time Series Econometrics at Yale University.’

Xiaohong Chen - Yale University

‘This is an excellent and current textbook, covering the classical topics such as ARMA modeling and spectral analysis, as well as the relevant recent developments such as inference using self-normalized processes, non-linear and machine learning methods. The textbook offers a perfect balance of mathematical rigor and clarity and provides numerous empirical examples.’

Bulat Gafarov - University of California, Davis

‘A state-of-the-art guide designed to introduce students to time series analysis, with an emphasis on its applications in economics and finance. The textbook combines traditional econometric techniques with modern data science approaches, making it a crucial tool for students in the fields of economics, finance, and statistics.’

Wolfgang Karl Härdle - Humboldt University of Berlin

‘This is an excellent textbook that provides a comprehensive and thoughtful introduction to modern econometric techniques using time series data. It is a great source for use in a graduate-level course. It will also be very helpful to applied researchers interested in working with macroeconomic or financial data.’

Anna Mikusheva - Massachusetts Institute of Technology

‘This is an excellent book for the reader learning the art of modern time series models with empirical relevance and applicability in economics and finance. This textbook synthesizes all the major recent advances and latest tools, ranging from Bayesian methods, nonparametric and semiparametric techniques to machine learning methods.’

Jiti Gao - Monash University

‘This textbook presents one of the most modern approaches to time series analysis I have seen. Covering everything from ARMA modeling to machine learning, it balances technical depth with clarity. Practical applications at the end of each chapter keep topics relevant. Ideal for graduate students or advanced undergraduates with a strong math background.’

Elena Pesavento - Emory University

‘Time Series for Economics and Finance is an invaluable resource for advanced students and professionals in economics, finance, and statistics. It offers a thorough exploration of modern time series techniques, including Bayesian methods and machine learning, tailored specifically to real-world applications. This textbook is essential for anyone looking to deepen their understanding of time series analysis in economic and financial contexts.’

Yoon-Jae Whang - Seoul National University