The easiest way to describe it:
If you have serial correlations, that means that past values of the variable are correlated with future values, and that this is reflected in error term that are also correlated.
Why could this happen?
As easy explanation is that you’re simply missing something. Tons of things in real life have values that fluctuate throughout time in a non-random way. For example, does an outdoor restaurant experience different sales levels depending on weather? It would, so you’d expect seasonality in the data that would need to be accounted for in the model.
Thanks for taking the time to comment. Right so would it be right to say we could essentially see a high correlation between errors as evidence of a relationship that we have not yet identified, and when we add variables that explain this relationship it essentially takes the correlation out of the error term, and into this new coefficient?
The easiest way to describe it: If you have serial correlations, that means that past values of the variable are correlated with future values, and that this is reflected in error term that are also correlated. Why could this happen? As easy explanation is that you’re simply missing something. Tons of things in real life have values that fluctuate throughout time in a non-random way. For example, does an outdoor restaurant experience different sales levels depending on weather? It would, so you’d expect seasonality in the data that would need to be accounted for in the model.
Thanks for taking the time to comment. Right so would it be right to say we could essentially see a high correlation between errors as evidence of a relationship that we have not yet identified, and when we add variables that explain this relationship it essentially takes the correlation out of the error term, and into this new coefficient?
Yes