Timeline for Could somebody explain to me what this ARIMA model output says?
Current License: CC BY-SA 3.0
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| Aug 20, 2012 at 21:35 | comment | added | Pr0no | I mean MAPE = 170,905 and MaxAPE = 12045,319 (Model Fit table), both of which were measures of uncertainty of the prediction. At least, that's how I understood it. | |
| Aug 20, 2012 at 21:28 | comment | added | Michael R. Chernick | Yes regarding determining the prediction formula. I am not sure where you get the 12% from. Several of the variables in the fit table are informative about how good the fit is . Also the R square of 0.286 means that approximately 29% of the variation in the response is explained by the model. That isn't great but it is still significant. All the coefficients in your model are statistically significant with p-values less than 0.001 except for one which is at 0.039. | |
| Aug 20, 2012 at 21:22 | comment | added | Pr0no | So basically from this output you can write a predictive formula like you would write an explainatory formula using OLS output? Also, am I right with the 12,000%? Or else what does it mean? Thanks for your help btw. I'm at my wit's end here :-) | |
| Aug 20, 2012 at 21:20 | comment | added | Michael R. Chernick | @Pr0no To get a prediction you take the covariates and the lagged observations, multiply them by their coefficients and add them up to predict the value one step into the future. | |
| Aug 20, 2012 at 21:16 | history | edited | Michael R. Chernick | CC BY-SA 3.0 |
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| Aug 20, 2012 at 21:15 | comment | added | Pr0no | Thanks Michael. What I understand is that SPSS only returns one model. This is why in the "Model Fit" table, all values are similar I guess. But I understood that the MAPE (mean absolute percentage error) is a measure of how much the DV varies from the model-predicted level. It therefore provides an indication of the uncertainty in the prediction. As I understand, in my case the uncertainty is 12,000%? That would be laughable (though not funny ;-) | |
| Aug 20, 2012 at 21:04 | history | answered | Michael R. Chernick | CC BY-SA 3.0 |