Relatively new to stats. I use linear regression and get R^2, which is quite low.
lmoutar=lm(formula = ts_y ~ ts_y_lag + ts_x)
So switched to arima with external regressor. Using "auto.arima", I formulate arimax model
fitarima <- auto.arima(ts_y, xreg=ts_x) arimaout<-arima(ts_y,order=c(2,0,5),xreg=ts_x)
How can I compare the explanability of AR model with arima model. From the thread How can I calculate the R-squared of a regression with arima errors using R?, I understand R^2 is not an option for ARIMA.
From the thread Model comparison between an ARIMA model and a regression model, AIC/BIC is not the right criteria and MSE from forcast/predict can be possible criteria for comparison across AR and ARIMA model. Is MSE the best option for model comparison, if so how would I generate MSE for AR and ARIMA?
I tried to compare the above ar and arima model using anova, but I get following error message
anova.lm(lmoutar,arimaout) Warning message: In anova.lmlist(object, ...) : models with response ‘"NULL"’ removed because response differs from model 1
What does this error message mean?
Thanks for the response so far and insight that AR is nested within ARIMA. How would one answer this question, if I rephrase as "How to compare AR, ARIMA and General Linear Models?". The first model I listed has AR(1) and independent variable; it is a general linear model. So how would I compare a GLM versus ARIMAX model? Any thing else besides MSE that I could use to judge between GLM and ARIMAX