Significance of variables in VAR model and forecasting
Posted: Thu Jul 21, 2016 11:03 am
Hello,
I would like to ask how to solve presence of statistically insignificant variables in VAR model.
I constructed VAR(4) model for two variables. Diagnostic control of residuals was satisfying. Nevertheless, there are some statistically insignificant variables in the models. I want to make forecast from this system of models. When I remove these insignificant variables, the forecasts are very poor and Root Mean Square Error very high compared to values of response variable. Should I forecast only from model with all variables included?
I think that presence of statistically insignificant variables in model does not necessarily mean that the model is not good. In standard one equation regression model I would throw away insignificant variable but in VAR model it is not so easy as for order 4 I cannot throw away variables of lag 3 when lag 4 is significant.
Thank you for any comments.
I would like to ask how to solve presence of statistically insignificant variables in VAR model.
I constructed VAR(4) model for two variables. Diagnostic control of residuals was satisfying. Nevertheless, there are some statistically insignificant variables in the models. I want to make forecast from this system of models. When I remove these insignificant variables, the forecasts are very poor and Root Mean Square Error very high compared to values of response variable. Should I forecast only from model with all variables included?
I think that presence of statistically insignificant variables in model does not necessarily mean that the model is not good. In standard one equation regression model I would throw away insignificant variable but in VAR model it is not so easy as for order 4 I cannot throw away variables of lag 3 when lag 4 is significant.
Thank you for any comments.