ECON 511 (Fall 2005)

Time series econometrics: Theory and applications

Instructor

T.A.

Time and place

Tuesday and Thursday 1-2:14 PM in 105 Walker

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 two weeks a number of theoretical and/or empirical exercises will be assigned as homework.

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 for Ph.D. students in economics

Textbooks

There is no required textbook: I will issue lecture notes. However, most of the material in this course is also covered by:

• [B] Bierens, H.J. (2004), Introduction to the Mathematical and Statistical Foundations of Econometrics, Cambridge University Press (Chapters 7 and 8 only). The draft of book has been used in ECON 501.
• [H] Hamilton, James D. (1994) Time Series Analysis, Princeton University Press

Topics

1. Stationary time series and limit laws [B: Ch. 7]
   • Hilbert spaces of random variables
   • The Wold decomposition
   • Weak laws of large numbers and consistency of M-estimators for stationary time series models [Lecture notes]
   • The martingale difference central limit theorem and asymptotic normality of M-estimators of stationary time series models
2. Dynamic regression models [H: Ch. 8]
3. Maximum Likelihood [H: Ch. 5; B: Ch. 8]
4. Stationary ARMA processes [H: Ch. 3, Lecture notes ]
5. Model selection on the basis of information criteria [Lecture notes]
6. Forecasting [H: Ch. 4; Lecture notes]
7. Vector autoregressions and innovation response analysis [Lecture notes; H: Ch. 11]
8. Unit roots [Lecture notes (Revised: November 11); H: Ch. 15+17]
9. Spurious regression [Lecture notes; H: Ch. 18]
10. Cointegration [Lecture notes; H: Ch. 19-20]
11. Nonlinear cotrending analysis (new topic) [Paper (see also the published version) with appendix]
12. ARCH and GARCH [H: Ch. 21, Lecture notes (under construction)]

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.