Modern strategies for time series regression

Statistical methods for the analysis and forecasting of time series data have a long history (Tsay, 2000). The well-accepted Box-Jenkins analysis and forecasting methods have been applied in a wide range of applications, from finance to medicine, and the classic book that laid out the theory is now in its fourth edition with over 55,000 citations (Box et al., 2015). In this paper, we focus on the specialized area of time series regression where the goal is to predict one time series with the help of covariates that include elements which also have a time series nature. Some authors refer to this as dynamic regression (Hyndman and Athanasopoulos, 2018), others use the term regARIMA (Gómez and Maravall, 1994; Maravall et al., 2016). Pankratz (2012) provides an excellent overview.