Timeline for Residual autocorrelation in VAR with non stationary data
Current License: CC BY-SA 3.0
4 events
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| Jan 21, 2018 at 22:34 | comment | added | HasVar | Actually I am going to proceed my analysis with running SVAR model with sign restrictions and then look at the impulse response functions. I guess in this case the autocorrelation is really a big problem. How can I solve it? Do I need to turn to VECM or VAR with stationary data? | |
| Jan 21, 2018 at 22:23 | comment | added | stans | Only if your goal is building a black-boxy model for relatively accurate forecasting out of sample... However, if your goal is to understand the mechanism behind the interplay of X and Y, capturing the codependence in residuals is important. | |
| Jan 21, 2018 at 22:20 | comment | added | HasVar | Thanks for your reply! I run two VARs. In the first one the variables are cointegrated. Is the autocorrelation ok in this case? In the second one some of the variables are cointegrated. What should I do in this case? Can the reason of autocorrelation be also an ommited variable? | |
| Jan 21, 2018 at 22:06 | history | answered | stans | CC BY-SA 3.0 |