After successfully completing the course, students will
1. understand the fundamental difference between time series 
processes and cross-sectional 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 autoregressive-moving 
average (ARMA) models and how to use such models for forecasting;
4. know how to specify and estimate models of time-varying 
conditional variance;
5. understand the implications for modeling and forecasting of 
non-stationary processes;
6. know how to estimate and specify multivariate time series 
models of stationary and non-stationary processes;
7. know when to consider and apply simple non-linear time 
series models;
8. have a working knowledge of the R programming language.

1. Preliminaries
   a. Introduction to time series
        i. time-series versus cross-section 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. auto-correlation, 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. Time-varying volatility (GARCH) models
   c. Univariate models of non-stationary 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 non-stationary processes
        i. cointegration
       ii. vector-error-correction models
4. Non-linear time series
   a. implications of non-linearity
   b. the bilinear model
   c. threshold AR models

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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.