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

Exercises (on your own or in tutorial)

Complete Exercises 1-7 from Section 7.10 of the book.

Solutions to Exercises

Slides for seminar

Download pdf

Seminar activities

  1. Fit a regression model with a piecewise linear trend and Fourier terms for the US leisure employment data.

    leisure <- us_employment |>
      filter(Title == "Leisure and Hospitality", year(Month) > 2001) |>
      mutate(Employed = Employed / 1000) |>
      select(Month, Employed)
  2. Add a dynamic regression model with the same predictors.

  3. How do the models compare on AICc?

  4. Does the additional ARIMA component fix the residual autocorrelation problem in the regression model?

  5. How different are the forecasts from each model?

R code used in seminar