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.