Timeline for A multivariate linear regression for explaining impacts of the predictors
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jul 18, 2019 at 19:41 | comment | added | Fisseha Berhane | but if d= 2, I am modeling y'' as a function of x'', not y as function of x. the coefficients I get in this case will show relationships between x'' and y' '. How do I get the coefficients with the original data? | |
| Jul 18, 2019 at 9:03 | comment | added | Leevo | If your ARIMA(1, 2, 1) is making the series stationary, then you can safely ignore the time series components of the regression equations, you only care about the external variables that you want to check and study their coefficients and SE as you proposed above | |
| Jul 18, 2019 at 8:25 | comment | added | Fisseha Berhane | so if use ARIMAX, which makes is stationary, and get coefficients, how do I interpret them? say I get ARIMA(1,2,1)? | |
| Jul 18, 2019 at 8:14 | comment | added | Leevo | Well, any course, textbook, source on time series analysis is good for this task. The first thing you always have to do before running a time series regression is to make the series stationary. That's all you need. Once you made it stationary, you just check the parameters that you're interested in. | |
| Jul 18, 2019 at 8:02 | comment | added | Fisseha Berhane | is there anonline example that does that to refer to? | |
| Jul 18, 2019 at 7:57 | history | answered | Leevo | CC BY-SA 4.0 |