Instructor

T.A./Grader

Time and place:

Tuesday and Thurday 1:00-2:15 PM in 121 Thomas.

Objectives and grading

The objective of this course is to prepare the Ph.D. students in economics for the study of empirical macroeconomics, by providing a rigorous introduction to the theory and practice of time series analysis (univariate as well as multivariate time series, and stationary as well as non-stationary time series).

Each week a number of theoretical and/or empirical exercises will be assigned as homework. The theoretical homework serves as preparation for the midterm and final exams, and the empirical homework will prepare you for the term paper.

The final grade will be determined by the homework (10%), a written closed-book mid-term exam (30%), a written closed-book final exam (30%), and an empirical term paper (30%). The final exam will cover the material of the mid-term exam as well. If you score higher on the final exam than on the mid-term exam, the latter score will be ignored, and the final exam will count for 60% of the final grade. The term paper is due on the final exam date.

Prerequisite level

ECON 501 and ECON 510

Textbooks

There is no required textbook, except for the Chapters 7 and 8 in

• [B] Bierens, H.J. (2004), Introduction to the Mathematical and Statistical Foundations of Econometrics, Cambridge University Press

Topics

1. Stationary time series and limit laws
   • Hilbert spaces of random variables [B: Appendix to Ch. 7 + New note on Hilbert spaces]
   • The Wold decomposition [B: Ch. 7]
   • Weak laws of large numbers and consistency of M-estimators for stationary time series models [B: Ch. 7 + Lecture notes]
   • The martingale difference central limit theorem [B: Ch. 7]
   • Consistency and asymptotic normality of M-estimators of stationary time series models [B: Ch. 7]
2. Stationary ARMA processes [Lecture notes]
3. More about univariate stationary time series
   • ARMA model selection on the basis of information criteria [Lecture notes]
   • ARCH and GARCH [Lecture notes (Section 15)]
   • Forecasting [Lecture notes: undergraduate and intermediate level]
   This material is for self-tuition. I will not give formal lectures about it, but only answer questions.
4. Maximum Likelihood estimation of time series models [B: Ch. 8]
5. Vector autoregressions and innovation response analysis [Lecture notes]
6. Unit roots
   • The Augmented Dickey-Fuller (ADF) and Phillips-Perron tests [Lecture notes]
   • The Breitung test [Paper]
   • Spurious regression [Lecture notes]
7. Cointegration
   • Introduction to cointegration [Lecture notes]
   • Theory of likelihood-based cointegration analysis [Lecture notes]

Theoretical homework assignments

1. Assignment 1
2. Assignment 2
3. Assignment 3
4. Assignment 4
5. Assignment 5

Empirical homework assignments

1. Assignment 1
2. Assignment 2
3. Assignment 3
4. Assignment 4

Exam dates

• Midterm: Thursday October 25
• Final: Tuesday, December 18, 10:10AM-12:00PM in 121 THOMAS

Disability Message

The Pennsylvania State University encourages qualified persons with disabilities to participate in its programs and activities. If you anticipate needing any type of accommodation in this course or have questions about physical access, please tell the instructor as soon as possible.