Week 11: Dynamic regression
What you will learn this week
- How to combine regression models with ARIMA models to form dynamic regression models
- Dynamic harmonic regression to handle complex seasonality
- Lagged predictors
Pre-class activities
Read Chapter 10 of the textbook and watch all embedded videos
Slides for seminar
Workshop activities
Seminar Code
Tutorial exercises
NoteTutorial Learning Objectives
- Fit dynamic regression with
ARIMA().- Able to specify and fit a regression model with ARIMA errors
- Able to assess model fit using standard regression diagnostics and ARIMA residual diagnostics; and to use the fitted model to forecast when future predictor values are known.
- Evaluate and interpret dynamic regression.
- Able to compare competing specifications using standard criteria;
- Able to interpret output from joint regression-and-error models
- Able to discuss strengths and limitations of the forecasts and the prediction intervals.
- Complete Exercises 1, 2, 4, 5, 6, and 7 from Section 10.7 of the book.
Assignments
- GA4 is due on Tuesday 26 May.