

Course ID:  ECON 4760/6760. 3 hours. 
Course Title:  Time Series Analysis 
Course Description:  An introduction to the statistical analysis of time series data.
Focus is on fundamental models of time series processes and how
these models can be used for forecasting and influence. Although
some statistical theory is necessary and will be developed, the
main thrust involves applying models to the data. Because data
analysis will rely on the R statistical programming language,
the basics of that language will also be covered. 
Oasis Title:  Time Series Analysis 
Undergraduate Prerequisite:  ECON(MARK) 4750/6750 
Graduate Prerequisite:  ECON(MARK) 4750/6750 
Semester Course Offered:  Offered every year. 
Grading System:  AF (Traditional) 

Course Objectives:  After successfully completing the course, students will
1. understand the fundamental difference between time series
processes and crosssectional data;
2. know the basic concepts of time series processes and how to
apply these concepts to time series data;
3. know how to specify and estimate autoregressivemoving
average (ARMA) models and how to use such models for forecasting;
4. know how to specify and estimate models of timevarying
conditional variance;
5. understand the implications for modeling and forecasting of
nonstationary processes;
6. know how to estimate and specify multivariate time series
models of stationary and nonstationary processes;
7. know when to consider and apply simple nonlinear time
series models;
8. have a working knowledge of the R programming language. 
Topical Outline:  1. Preliminaries
a. Introduction to time series
i. timeseries versus crosssection samples
ii. stochastic processes
iii. time series models
iv. forecasting and inference
b. Fundamental concepts in time series analysis
i. sequences and convergence
ii. stationarity and ergodicity
iii. autocorrelation, serial independence, and white
noise
iv. review of linear regression with time series
samples
2. Univariate time series models
a. ARMA models
i. AR models and stationarity conditions
ii. MA models and impulse response functions
iii. specification and estimation
iv. forecasting
b. Timevarying volatility (GARCH) models
c. Univariate models of nonstationary processes
i. deterministic trends
ii. stochastic trends, unit roots, and ARIMA models
3. Multivariate time series models
a. VAR models
i. notation and the nature of multivariate extensions
ii. specification and estimation
iii. innovation accounting
iv. forecasting
b. Multivariate models of nonstationary processes
i. cointegration
ii. vectorerrorcorrection models
4. Nonlinear time series
a. implications of nonlinearity
b. the bilinear model
c. threshold AR models 
Honor Code Reference:  UGA Student Honor Code: "I will be academically honest in all
of my academic work and will not tolerate academic dishonesty
of others." A Culture of Honesty, the University's policy and
procedures for handling cases of suspected dishonesty, can be
found at www.uga.edu/ovpi. 