Hello all,
I would appreciate if somebody could advice on the best approach to forecast time series (out-of-sample) using ARIMA. My data has 528 observations, mean (-.0016047) and st. dev (1.282545). I have conducted initial tests and through logging and first differencing I made sure that the data is stationary (has a mean reversal property), there is no serial correlation and also cleaned my sample from the outliers. With help of the partial autocorrelation correlogram, I identified the optimal lagged terms to run the ARIMA regression and ended up with “arima lna, arima(6,1,2)”.
Questions:
- Please advice on the commands in STATA to help me forecast the time series for period T+1 using the aforementioned ARIMA model with 95% and 99% confidence level bands.
- How would you recommend an overall approach to back-test the accuracy of the model using the historical data? In other words, how can I see that my ARIMA predictions fit the actual historical data.
- At last, please recommend on the intuitive interpretation of the AIC and BIC for choosing the right model. Should I choose the model that has the lowest value of AIC or BIC.
Kind regards,
Joshua
Comment