I know for ARIMA models in R, there is an order parameter. I want to create a diverse set of ARIMA models by modifying the p,q,and d terms. I have a for loop that applies ARIMA models to a time series in this order: ARIMA(1,1,1) ARIMA(2,2,2) ARIMA(3,3,3) ARIMA(4,4,4) ARIMA(5,5,5). Does this make any sense statistically? -- View this message in context: http://r.789695.n4.nabble.com/ARIMA-models-tp2957129p2957129.html Sent from the R help mailing list archive at Nabble.com.
To me what is looking most exotic is the different orders of integration of your models, which you are assuming starting from 1 through 5. All asymptotic results regrading the distribution of the model parameters based on the fact that original DGP has exactly 1 as the order of integration, because most of the real life scenarios which are non-stationary in nature can be well approximated with that. Therefore perhaps usual t-values can not be justified once you cross the limit as 1. Apart from that, in my belief you can just go ahead with different combinations of p and q parameters and choose the optimal one (based on some pre-fixed criteria like AIC/BIC or non-significance of model coefficients). However in each experiment you should fix the initial values of the time series and should keep it same for all experiment. Best regards, -- View this message in context: http://r.789695.n4.nabble.com/ARIMA-models-tp2957129p2964483.html Sent from the R help mailing list archive at Nabble.com.