– Double check example questions attached before making an offer, the worksheet questions are difficult.
– 80%+ grade needed.
– Answer questions on paper or in word.
– Notes and exercises will be provided.
Topics included:
1. Overview. Stationarity, outline of Box-Jenkins approach through identification of model, fitting, diagnostic checking, and forecasting. Mean, autocorrelation function, partial autocorrelation function.
2. Models. Autoregressive (AR) models, moving average (MA) models, ARMA models, their autocorrelation functions, and partial autocorrelation functions. Transformations and differencing to achieve stationarity, ARIMA models.
3. Estimation and diagnostics. Identifying possible models using autocorrelation function, and partial autocorrelation function. Estimation, outline of maximum likelihood, conditional and unconditional least squares approaches. Diagnostic checking, methods and suggestions of possible model modification.
4. Forecasting. Minimum mean square error forecast and forecast error variance, confidence intervals for forecasts, updating forecasts, other forecasting procedures.
5. Seasonality, time series regression.
6.The frequency representation of a stationary time series.
7. The use of a periodiogram to carry out harmonic analysis.
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