multiple regression for forecasting : considerations

 

1. Forecasts will be unreliable if the values of the future predictors are outside the range of the historical values of the predictors. For example, suppose you have fitted a regression model with predictors X and Z which are highly correlated with each other, and suppose that the values of X in the fitting data ranged between 0 and 100. Then forecasts based on X>100 or X<0 will be unreliable. It is always a little dangerous when future values of the predictors lie much outside the historical range, but it is especially problematic when multicollinearity is present.

Note that if you are using good statistical software, if you are not interested in the specific contributions of each predictor, and if the future values of your predictor variables are within their historical ranges, there is nothing to worry about — multicollinearity is not a problem.

 

 

1. Forecasts will be unreliable if the values of the future predictors are outside the range of the historical values of the predctors. For example, suppose you have fitted a regression model with predictors and which are highly correlated with each other, and suppose that the values of in the fitting data ranged between 0 and 100. Then forecasts based on or will be unreliable. It is always a little dangerous when future values of the predictors lie much outside the historical range, but it is especially problematic when multicollinearity is present.

Note that if you are using good statistical software, if you are not interested in the specific contributions of each predictor, and if the future values of your predictor variables are within their historical ranges, there is nothing to worry about — multicollinearity is not a problem

 

 

- Otexts, Hyndman  -- Multiple Regression  (https://www.otexts.org/fpp/5/7  ::  ( 5.7 Correlation, causation and forecasting : Correlation is not causation)