Timeline for Minimizing variance when searching for Cointegration
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Aug 6, 2018 at 14:02 | vote | accept | JeanGuillaume | ||
| Aug 6, 2018 at 13:36 | comment | added | stans | Yes, you get two different answers for X1 ~ X2 and X2~ X1. This is related to one of the properties of least squares estimation. However, both vectors are consistent estimates of the cointegrated relationship. To identify the better one, you can run ADF scoring on the residuals. | |
| Aug 6, 2018 at 10:34 | comment | added | JeanGuillaume | Empirically, regressing X1 on X2 and regressing X2 on X1 does not give the same result. Is it because I have a finite number of observations ? With an infinite number I would get the same result ? Ok I understand the link with regression. The eigenvector associated with the smallest eigenvalue is the vector which leads to the smallest variance and so to the minimum of a least square problem, right ? Thank you for your help. | |
| Aug 4, 2018 at 17:43 | answer | added | NBF | timeline score: 0 | |
| Aug 4, 2018 at 12:09 | comment | added | stans | This is related to the fact that, if X1 and X2 are cointegrated, regressing X1 on X2 leads to a consistent estimate of the cointegration vector. And regression is looking for least squares, remember? | |
| Aug 3, 2018 at 8:00 | history | edited | Bob Jansen♦ | CC BY-SA 4.0 |
Clean up
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| Aug 3, 2018 at 7:40 | review | First posts | |||
| Aug 3, 2018 at 7:53 | |||||
| Aug 3, 2018 at 7:37 | history | asked | JeanGuillaume | CC BY-SA 4.0 |