Time Series A First Course with Bootstrap Starter 1st Edition

Time Series: A First Course with Bootstrap Starter provides a comprehensive introductory course on time series analysis tailored for upper-level undergraduate and M.S. students. Written by Tucker S. McElroy and Dimitris N. Politis, this book strikes a balance between mathematical rigor, computational implementation, and accessibility. It covers essential theoretical concepts along with practical examples and exercises using the R programming language.

The book delves into foundational time series methods such as linear filters, geometric prediction approaches, ARMA models, frequency domain techniques, and information theoretic concepts like entropy. The authors emphasize the significance of geometric and frequency domain perspectives, introduce entropy maximization, and explore modern computer-intensive techniques like subsampling and the bootstrap method for handling nonlinear time series data.

With over 600 exercises, including R coding assignments and data analysis tasks, readers are equipped with the tools to master time series analysis. The book also offers supplementary resources on its website, including key datasets, R code for examples, and solutions to exercises.

About the Authors

Tucker S. McElroy, a Senior Time Series Mathematical Statistician at the U.S. Census Bureau, brings over 15 years of expertise in time series research and software development. He has published extensively and received accolades such as the Arthur S. Flemming award.

Dimitris N. Politis, a Distinguished Professor of Mathematics at the University of California, San Diego, and Associate Director of the Halıcıoğlu Data Science Institute, is a renowned scholar with numerous research publications and prestigious awards, including the Tjalling C. Koopmans Econometric Theory Prize.

If you are looking to delve into the intricacies of time series analysis with a focus on computational methods and real-world applications, Time Series: A First Course with Bootstrap Starter is a valuable resource for both students and practitioners in the field.

FAQs

1. What background knowledge is required to benefit from this book?

A basic understanding of mathematical statistics is recommended to fully grasp the concepts presented in the book.

2. Are solutions provided for the exercises in the book?

Yes, the book offers solutions to more than 600 exercises on its website, along with key datasets and R code for examples.

3. How does the book address nonlinear time series data?

The book explores modern techniques like the bootstrap method to handle nonlinear time series data, where traditional methods may fall short.

Conclusion

Time Series: A First Course with Bootstrap Starter offers a rigorous yet accessible approach to mastering time series analysis. With a blend of theoretical foundations, practical examples, and computational exercises, this book equips readers with the essential tools to analyze and interpret time series data effectively. Whether you are a student or a professional seeking to enhance your understanding of time series methods, this book serves as a valuable resource in the field.