ECO 722, Time Series and Forecasting
Fall 2014
1. Course Objective: To build on your econometric skills; to introduce you to concepts and
techniques in forecasting and time-series econometrics; and to implement the SAS software for timeseries analysis to help you earn a certification in SAS.
2. Vital Data:
Instructor: Andrew Brod
Class rooms: 1:00-2:15 M in Bryan 117; 1:00-2:15 W in MHRA 1305 lab
Office: 467 Bryan Building, phone 707-6439, e-mail AndrewBrod@uncg.edu
Office hours: 2:15-3:15 MW, or by appointment
3. Class Materials: (1) Time Series Analysis, with Applications in R (2nd ed.), by Jonathan D. Cryer
and Kung-Sik Chan. (2) SAS for Forecasting Time Series (2nd ed.), by John C. Brocklebank and David
A. Dickey.
4. Grading System: Your course grade will be based on the following: Two assignments (each worth
10%), a quiz (worth 30%), and a final exam/project (50%). My grade cut-offs will be 87.5 points for an
A and 75 points for a B. I reserve the right to award, at my discretion, a letter grade above that indicated
by a student’s numerical score on the basis of demonstrable improvement and hard work.
5. Quiz Date: The quiz will be held in class on Monday, October 6.
6. Final Exam Date: Wednesday, December 3, 3:30-6:30 p.m.
7. Quiz Policy: The quiz will be a closed-book quiz, except that you will be allowed to refer to notes
you write (no computer output!) on one (1) 8½x11 sheet of paper. In addition, I’ll provide you with
copies of statistical tables, if relevant. There will be no make-ups for missed quizzes. However, I will
add the grade weight from any missed quiz to that of your final exam if (and only if) you provide me with
a valid reason for missing it.
8. Final Exam Policy: The final will cover all material in the course, although more weight will be
placed on material covered since the quiz. Like the quiz, the final will be a closed-book exam, with the
two exceptions listed above. However, I may make all or part of the final a take-home project.
If you miss the final, you must notify me in writing as soon as possible, providing an explanation of why
you missed it. You should include evidence of your claim, such as a receipt from the towing service, a
note from Academic Advising, or a note from your physician. If I deem your explanation to be legitimate
and timely, I will allow you to take a make-up final. If you miss the final without a legitimate excuse,
you will receive a zero grade. If you know ahead of time of a conflict, tell me about it in writing. If your
request is reasonable and you get it to me by the last regular class meeting, I’ll allow a make-up final.
9. Bad Weather Policy: Neither classes nor quizzes will be cancelled unless the University is
officially closed. In case of bad weather, listen for a cancellation announcement on radio or TV. If we
have a quiz scheduled for a day on which the University closes, the quiz will be rescheduled for the next
class meeting.
10. Class Attendance: This is a graduate class. Attendance is your decision, period.
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11. Honor Code: Students are expected to know and abide by the UNCG Academic Honor Code.
12. Course Outline: In what follows, CC indicates the Cryer-Chan text, while BD indicates the
Brocklebank-Dickey manual.
A. Introduction to forecasting:
CC chapters 1-2
BD chapter 1
Lab: Introduction to SAS
Accessing data and the DATA step, using PROCs, some simple univariate models.
B. Regression in a time-series framework:
CC chapter 3
Focus especially on Sections 5.4, 6.6 and Appendix 7A
Lab: REG, AUTOREG, CORR
OLS, stochastic regressors, instrumental variables, autocorrelated errors, feasible GLS
C. Smoothing techniques
Materials to be provided
Lab: FORECAST
Exponential smoothing, Holt-Winters methods, seasonal effects and trends
D. Univariate models:
CC chapters 4-6
BD chapters 2-4
Lab: ARIMA
ARs, MAs, ARMAs, ARIMAs, differencing, unit roots, ACF and related tools, information
criteria
E. ARIMA topics:
CC chapters 9-10
BD chapter 5
Forecasting, prediction limits, cointegration
F. Limited ARCH models:
CC chapter 12
BD chapter 5
ARCH, GARCH, applications
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