Seasonal Adjustment in R

Welcome

This is the website for the work-in-progress edition of Seasonal Adjustment in R, an online Book by James Livsey and Christoph Sax.

About the book

This book will teach you how to do seasonal adjustment in R, using X13-ARIMA-SEATS.

Specifically, the audience will be both R users who want to learn about seasonal adjustment as well as seasonal adjustment practitioners, who are interested in using R. The book will be tailored to the practical applications of seasonal adjustment within R. It presents background material and references for the theoretically minded reader. The main focus, however, is on concrete problems and examples.

We will showcase methods through detailed examples with associated code. This presentation allows the academic level to be quite broad; understood by undergraduates all the way through advanced Ph.D. students.

Key features of the book

  • Each chapter include a concrete practical problem and shows how X-13 can be used to address it

  • Teach-by-example format

  • Continuous connection of X-13ARIMA-SEATS input with R input and vice-versa

  • Fundamental theoretical material is referenced throughout (mainly as an option)

  • For each example given the book will give answers, code and provide data

Certainly! Here’s a refined version of the section describing the specialized boxes used throughout your book:

Specialized Content Boxes

We employ a variety of specialized content boxes to enhance the reading experience. Each type of box serves a unique purpose, catering to different reader interests.

Case Study

These boxes present practical applications of specific topics discussed in the book. They feature a wide array of time series examples, varying in geographical and topological contexts. Case studies are designed to be universally appealing, offering insights for all readers.

Fundamentals

To gain deeper insights, it is sometimes interesting to derive of X-13 from basic fundamentals. Instead of solely depending on X-13 for analysis, we explore key concepts through R functions. This approach is particularly engaging for readers who are proficient in R.

Frequency Domain

Time series analysis in the frequency domain provides a powerful perspective for understanding time series behaviors. These boxes introduce and explain the applications and advantages of frequency domain methods in time series analysis. If you are unfamiliar with the frequency domain, you can safely skip these sections.

Exercises

Each chapter concludes with a series of exercises. These are designed to reinforce the material covered and test your knowledge. They are particularly valuable for readers using this book in an academic course or for self-study.