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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.

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  • $\begingroup$ 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. $\endgroup$
    – Richard Hardy
    Commented Nov 4, 2015 at 20:07
  • $\begingroup$ That was a typo; I've updated the question. Thank you. $\endgroup$
    – Rick Arko
    Commented Nov 4, 2015 at 20:48

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See the Algorithms doc available from the Help menu for computational details. If you have Statistics version 23, you might also be interested in the TCM procedure (Analyze > Forecasting > Create Temporal Causal Models)

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  • $\begingroup$ Thank you. It appears as though the series of predictor variables are added to the transfer function model. (I would be interested to know if there is a way to automatically fit an ARMA process to regression residuals as the forecast package in R does.) I read over some material on Temporal Causal Models and haven't decided if I'll be able to make use of that functionality yet. Thanks for the suggestion. $\endgroup$
    – Rick Arko
    Commented Nov 5, 2015 at 22:38

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