# ARIMAX Forecasting in SPSS vs. R

I'm using time series data containing both trend and seasonality. I also have 2 endogenous predictor variables that I would like to include in my model.

In R I've used the forecast package to develop a dynamic regression model with use of auto.arima() and the xreg argument from the forecast package. I understand this procedure takes a regression and then attempts to fit the residuals with an ARMA Model.

I've also developed what seems to be an appropriate model using the forecasting Module in SPSS by specifying a Seasonal ARIMA model and including my covariates. However, one of the coefficients on one of my endogeneous predictors has a negative sign which makes no sense intuitively.

I've read Dr. Hyndman's article The ARIMAX model muddle and found it to be extremely insightful and useful. However, I have not been able to find any documentation on what type of statistical procedure SPSS uses to fit an ARIMA model with covariates, so I'm not sure how I should interpret the coefficients or how concerned I should be with a flipped sign. Any help clarifying the modelling procedure used by SPSS would be tremendously appreciated.

• You say I understand this procedure takes a regression and then attempts to fit the residuals with a linear regression but that's probably a typo since it is ARMA model that is used to fit the residuals. – Richard Hardy Nov 4 '15 at 20:07
• That was a typo; I've updated the question. Thank you. – Rick Arko Nov 4 '15 at 20:48