I constructed a VAR model of order 4 where some of the variables are statistically insignificant. The model is based right in terms of diagnostics (no autocorrelation of residuals, normal distribution, homoskedasticity). I then removed the statistically insignificant variables and computed forecasts.
However, when I evaluate the quality of forecasts using root mean square error (RMSE), I obtain very high values; for example, the response variable has values around 100 while RMSE comes in at around 50.
Is it possible?
How to solve such a conflict: I have good model and such poor forecasts?
Did I make mistake somewhere?