Syllabus of STAT 685, Summer 2007
H. Joseph Newton
Professor of Statistics and Dean of Science
Texas A&M University
Class: TTh, 1:303:00, Blocker 517
Office: 517 Blocker Building.
Hours: Daily, 1:002:00, or by
appointment.
Phone: (979) 8458817.
FAX: (979) 8456077.
Email:
jnewton@stat.tamu.edu
Address:
Statistics Department ,
Texas A&M University ,
College Station TX 778433143 USA.
Prerequisites
Knowledge of random variables, moments, distribution theory, maximum
likelihood estimation, and regression analysis (using matrices) such
as covered in STAT 601 is assumed; complex numbers and trigonometry.
Course Materials
The text for the course is a reprint of ``TIMESLAB: A Time Series
Analysis Laboratory,'' written by the instructor and originally
published by Wadsworth & Brooks/Cole. Try to get copies from people who
took the course before.
A set of course notes is available on the course web page under
``Topic pdf files'' on
the ``Course notes'' page.
The software for the course is a translation of the timeslab software that accompanies the
text into the freely available R language (R is available for free download to
PCs)
Determining the Course Grade

Two inclass, closedbook exams (worth 30\% each) and a final
project worth 40%;
Homework (primarily analyzing real time series) worth total of 40%.

We will not have a final exam.

The last day of class will be determined (early in August).
Course Outline
 Lecture 1
Introduction; Correlogram, Partial Correlogram
(Section 1.11.4.2)
 Lecture 2: Introduction to the TIMESLAB program; Periodogram
(Appendix B, Section 1.4)
 Lecture 3: Periodogram (continued); Transforming data
(Section 1.5)
 Lecture 4: Simple forecasting methods (Section 1.6)
 Lecture 5: Difference equations (Section 1.6)
 Lecture 6: Review for exam 1
 Lecture 7: Exam 1: Covers Chapter 1
 Lecture 8: Covariance stationary time series
(Sections 2.12.2)
 Lecture 9: Linear filters (Section 2.3)
 Lecture 10: Theory of prediction (Section 2.4)
 Lecture 11: Random walks; ARMA processes (Section 2.5)
 Lecture 12: ARMA processes (continued)
 Lecture 13: Statistical properties of descriptive
statistics (Section 3.1)
 Lecture 14: Tests for white noise (Section 3.2)
 Lecture 15: Smooth periodogram spectral estimation (Section 3.3)
 Lecture 16: Review for exam 2
 Lecture 17: Exam 2: Covers through Section 3.3
 Lecture 18: Fitting ARMA models (Sections 3.43.5)
 Lecture 19: Fitting ARMA models; BoxJenkins
forecasting (Section 3.6)
 Lecture 20: BoxJenkins forecasting and other modeling
strategies (Sections 3.63.7)
 Lecture 21: Searching for periodicities (Sections 3.83.9)
 Lecture 22: Bivariate time series (Section 4.1)
 Lecture 23: Coherence, phase, and gain (Sections 4.14.2)
 Lecture 24: Additional topics
 Lecture 25: Last class; Final project reports