Course Director:Joann Jasiak
This course is an introduction to time series analysis
for graduate students who have taken ECON5025 or an equivalent course.
It will cover the basics of time series analysis such as the
definition of a stochastic process, the stationarity and the autocorrelation function (ACF).
These concepts will be used to examine selected
univariate autoregressive and moving average processes (ARMA), and autoregressive conditional
heteroskedasticity (ARCH) processes.
Students will learn to identify and estimate stationary univariate models,
detect the seasonality, and test for nonstationarity in the trend
from the D-F unit root tests. Later on, a multivariate VAR model will be discussed along
with the concept of cointegration and the VEC model.
concepts introduced in this course will be illustrated in class by various
empirical examples. Additional examples will be assigned as homeworks.
Students are encouraged to work and submit their assignments in teams of no
more than 3 participants. Most assignments will require some basic programming
skills. In class, students can learn SAS, and use the SAS codes available on
the course website.
Requirements, Evaluation and Other Details IS CHANGED: SEE MOODLE FOR DETAILS!
1. Mid-term exam: 30% approximate date of exam: February 27. It can be improved by submitting Assignment 4 individually.
2. Final exam: 46% (date to be determined)
3. Assignments 1,2 and 3: 24% available on-line from "Handouts". The solutions need to be handed in on February 6, March 6, and the last day of classes.
Books and Other Materials
William S. Wei, Time Series Analysis; Univariate and Multivariate Methods,2nd ed. (2006) Pearson.
Suggested for complementary readings:
R.C. Hill, W.E Griffith, G.C. Lim J.M. Principles of Econometrics, 3rd or 4th ed. (2008 or 2011) Wiley.
1. lecture notes at http://www.jjstats.com
1 Time series : examples and basic concepts (chap 1 and 2)
2. Stationary Time Series : estimation and tests; (chap. 3)
3. Nonstationary TimeSeries : ; (chap. 4)
4. Forecasting , ; (chap. 5)
6. Parameter Estimation ; (chap. 7)
7. Seasonal Models and Intervention Analysis
8. ARCH-GARCH Models ; (chap. 15)
9. Multivariate TS Models ; (chap. 16)
10. Cointegrationand ECM Representation ; (chap. 17)