The first assumption of the classic linear regression model is that the dependent (or outcome) variable can be calculated as a linear function of a specific set of independent (or predictor) variables, plus a disturbance term1. This statement implies that the unknown parameters of this linear function are stable or constant over the estimation period. This assumption is particularly important when using a linear regression model to predict.
In practical terms you are assuming the effect of your predictor(s) remains unchanged over the sample period; for relationships between financial variables this may be unrealistic in the presence of large ‘landscape changing’ events. In econometric terms these large events are sometimes called break points.
This instructive video explains in more detail how to test for parameter stability, both when a break point is known and when we cannot clearly identify a break point
- See Chapter 3 of Kennedy 1998 “A Guide to Econometrics” for an excellent introduction to the five assumptions underpinning classic linear regression models ↩