Following the post here, it has been an intense couple of weeks trying to understand ARIMA and how to apply exogenous regressors to the model. To summarize, I have attempted to forecast monthly unemployment data (in percentage) during several years using ARIMA and using viewership data of some Wikipedia articles as my exogenous regressors. Both, the time series and the regressors, have the same length. In many occasions, the addition of exogenous regressors improves the prediction of unemployment (in this case, 5 months of unemployment) obtained only using ARIMA without regressors. We have tried to test the robustness of this model by shifting back in time one month at a time. Taking care to keep at least 3 years of training and always forecasting 5 months. We noticed that the accuracy changes considerably.
We now have thought that perhaps the use of regressors is not appropriate because the "viewership time series" may not be completely independent. So we have considered using ARIMAX and transfer functions. The idea is to use both unemployment and viewership data to forecast unemployment. It is in this part that I am confused ...
Do you know any example of how to implement transfer functions using ARIMAX in R?
Do you think this is the right approach or should I stick with ARIMA and exogenous regressors?