I’ve been reading Time Series Analysis (‘Hamilton’) for 6 months. Today I officially finished reading the book. Last year, I finished Microeconomic Theory (’MWG’, Microeconomic Theory) and Time Series Analysis, both of which have greatly transformed my understanding regarding economic theory.This is the first time I’ve read a textbook so thoroughly and even solved every single problem after each chapter since college. I read it on numerous subway journeys to home, to school and to office, standing mostly. I read it in the beloved Old Hall of Tsinghua Library, during class breaks at Wudaokou, and at my office desk when I’ve done my work as a central banker. I read it late into the night, when my family all fell asleep, only the dim light from desk lamp as my sole companion. Hardcover Hamilton became softcover and covered by adhesive tape. A white-turned-grey Hamilton of 799 pages and a solution manual of 63 pages are the by-products. Although the manual contains many errors and some proofs are not as simple as appendix, when looking at it, as well as the book itself, it feels amazing. I’ve finally done it.Some thought as Ph.D., we should read papers instead of textbooks. It’s also ‘boring’ and sufficiently daunting to read those monographs. But as a newcomer to economic theory, considering my background of both math/physics and finance, I choose to start my career as an economist by reading classics. Every Ph.D. should be responsible for his own training. After reading several classic text book written by economics gurus, I’m so glad that I’ve made the right decision.Hamilton is not only about time series, but also major areas of econometrics. I found it much more superior than any book of econometrics I’ve read. It covers maximum likelihood estimation, asymptotic theory, general least squares, VAR, Bayesian Analysis, General Method of Moments, Cointegration, ARCH, GARCH, IGARCH and many other general topics covered in advanced econometrics courses.It’s cogent, coherent, rigorous, and most importantly, beautiful. I can’t talk the beauty of Hamilton, but I can name several important chapters. First several chapters are easy and pieces of cakes. Chapter 5 shows abundant numerical optimization techniques, which will blow up your mind for the first time. Chapter 7 is about asymptotic theory. This is the heart of advanced econometrics and repeatedly referenced to through the book. Chapter 8 instilled a whole semester of Advanced Econometrics I which we took last year into 28 pages. These two chapters are the next major blow-your-mind point. Chapter 13 (Kalman Filter) is the first major obstacle readers might encounter. Chapter 17 and 18 cover asymptotic theory for nonstationary time series. Chapter 17 and 19 are not only long, but also freaking difficult. Chapter 20 wraps up nonstationary time series. I find math proof in it truly splendid. Chapter 21 and 22 are the last chapters and written like poems, or musical notes. Yes, sipping through ARCH, GARCH, IGARCH, EGARCH is turned into poem-reading by Mr. Hamilton. I thank him very much for this. For so many years, when I heard about any-ARCH, I frowned. Now I’m more than happy to hear the ARCH family.Hamilton is hard. Reading speed diverged much during last 6 months. I could finish tens of pages per day, but most of the time, only several pages per day. When reading Chapter 19, I found it so hard that I forgot what the just turned page told. In Chinese, we call it ‘Duanpian('')’. For most of the chapters, I must read more than 3 times to gain a basic understanding. I read a little bit slowly not because Mr. Hamilton is a bad writer, but because the content itself. If you have read Greene’s Econometrics Analysis, you’ll find Hamilton more Ph.D.-friendly.Once when I was asked about what books to choose for the entrance exam of Ph.D. of PBC School of Finance in Tsinghua University, I would tell them several econometrics textbooks written in Chinese, such as CHENG Qiang’s or JIN Yunhui’s. Now I will definitely recommend Hamilton.Time Series Analysis by James D. Hamilton is simply the green card to econometrics.