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I am creating two models, where the fitted values of one SARIMAX type model are used as an exogenous predictor for a second dynamic regression model. I want to understand what assumptions of the gauss markov theorems or any other relevant theorems I am violating to do this. I am doing this process, because if I model the predictors based upon their passed lagged and seasonal lagged values, it appears to be a better way to forecast out future values of these predictors, which I need to forecast the dependent variable in the dynamic regression model. Using detailed diagnostics per Pankratz is not what I can afford to do in my situation, I need to create more than a 100 different models within a for loop, using the same two tiered model frame work. I want to be aware of the type of biases, issues or best practices related to the tasks described above. I checked for similar questions on crossvalidated, the most similar ones do not have answers.

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  • $\begingroup$ This paper might be relevant. It's very difficult to obtain jasa papers for free. If you're interested, maybe email the author and ask if he could provide a pdf. I had it at one point but I have no idea where it is right now. tandfonline.com/doi/abs/10.1080/01621459.1979.10481635 $\endgroup$ – mlofton Aug 8 '20 at 2:14
  • $\begingroup$ thanks for the article. $\endgroup$ – rer50 Aug 8 '20 at 13:22
  • $\begingroup$ @ rer50: I hope that it's relevant. $\endgroup$ – mlofton Aug 9 '20 at 14:39

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