- Lecture 1 (June 3): Introduction, Correlogram, Partial Correlogram (Section 1.1-1.4.2)
- Lecture 2 (June 5): Introduction to the TIMESLAB program; Peridogram (Appendix B, Section 1.4)
- Lecture 3 (June 9): Periodogram (continued); Transforming time series (Section 1.5)
- Lecture 4 (June 10): Simple forecasting methods (Section 1.6)
- Lecture 5 (June 12): Difference equations (Section 1.6)
- Lecture 6 (June 17):
Covariance stationary time
series (Sections 2.1-2.2)
- Lecture 7 (June 19): Exam 1: Covers Chapter 1
- Lecture 8 (June 24): Linear filters (Section 2.3)
- Lecture 9 (June 26): Theory of prediction (Section 2.4)
- Lecture 10 (July 1): ARMA Processes (Section 2.5)
- Lecture 11 (July 3):
ARMA processes (continued)
- July 7: No class
- Lecture 12 (July 8): ARMA processes (continued); Statistical properties of descriptive statistics (Section 3.1)
- Lecture 13 (July 10): Statistical properties of descriptive statistics (continued); Tests for white noise (Section 3.2)
- Lecture 14 (July 14): Nonparametric spectral density estimation (Section 3.3)
- Lecture 15 (July 15): Finding models and estimating their parameters (Sections 3.4-3.5)
- Lecture 16 (July 17):
**Exam 2:**Covers through Section 3.3 - Lecture 17 (July 22): Finding models and estimating their parameters (continued) (Section 3.6)
- July 22: Last day to Q-drop
- Lecture 18 (July 24): Finding models and estimating their parameters (continued) (Section 3.6)
- Lecture 19 (July 29): Regression with autocorrelated errors (Section 3.7); Searching for periodicities (Sections 3.8-3.9); Bivariate time series (Section 4.1)
- Lecture 20 (July 31): Coherence, phase, and gain for bivariate series (Sections 4.1-4.2)
- Lecture 21 (August 5): More on bivariate series (Section 4.3); Final project reports
- Lecture 22 (August 7):
**Last class;**More final project reports