Essentials Of Time Series Econometrics
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Essentials of Time Series for Financial Applications
Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. - Provides practical, hands-on examples in time-series econometrics - Presents a more application-oriented, less technical book on financial econometrics - Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction - Features examples worked out in EViews (9 or higher)
Essentials of Time Series Econometrics
"Essentials of Time Series Econometrics" explores the fundamental principles, methodologies, and practical applications of time series analysis in economics, finance, and related fields. Designed for students, researchers, and practitioners, this guide covers both theoretical foundations and practical techniques used to analyze temporal data and make informed decisions. We cover a wide range of topics, including basic concepts such as stationarity and autocorrelation, as well as advanced techniques like machine learning approaches, Bayesian analysis, and high-frequency data analysis. Each chapter provides clear explanations of key concepts, methodologies, and mathematical principles. Real-world examples and case studies illustrate the application of time series analysis in various domains. Hands-on exercises and practical assignments reinforce understanding and develop analytical skills. Contributions from leading experts ensure readers benefit from the latest research findings. A companion website offers additional resources, including datasets, code examples, and supplementary materials. This book is ideal for students, researchers, and practitioners looking to build a solid foundation in time series econometrics or apply advanced techniques to real-world problems.
Time Series Econometrics
In this book, the author rejects the theorem-proof approach as much as possible, and emphasize the practical application of econometrics. They show with examples how to calculate and interpret the numerical results. This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger and Newbold, and Nelson and Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot and Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Finally, students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.