Timeline for Interpreting VAR impulse response
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Dec 18, 2016 at 4:58 | comment | added | wizlog | @Sympa how is "one unit" defined? when I ran an irf on three variables each of the same unit, they each shifted by a very different amount for month zero. | |
| Mar 24, 2016 at 4:04 | comment | added | Sympa | The scale of your variable does not really affect the power of the response to the impulse. In each case, a one unit change in X causes a 4 unit change in Y. That's true whether your variables are in % or in units. | |
| Mar 23, 2016 at 20:06 | comment | added | Arvo P. | So basically, the way I see it (and where I probably somehow get lost) is that in the first case, the impulse is one hundred times the impulse in the 2nd case, as "+1 unit in x" depends also on the scaling. But the response is the same (0,04, 4%). Which interpretation would you consider correct in this case: impulse 1 (as 100%) --> response 0,04 (as 4%). Or impulse 1 (as 1%) --> response 4 (as 4%) | |
| Mar 23, 2016 at 19:59 | comment | added | Arvo P. | Thanks Sympa for your effort! Yes, I'm using vars package and irf(). My variables are on the same basis within the cases (in the 1st case, percentage points as: 0,01, 0,02... for both x and y. In the 2nd (scaled) case, the same percentage points presented as 1%, 2% for x and y). In the 1st case, why doesn't the "one unit increase in x" mean x -> x+1, e.g. 0,01 -> 1,01, i.e. +100%? For this impulse (whatever the magnitude), the response of y as given by irf() is "0,04". +4%?. In the second case, why isn't the one unit increase in x now +1%. For this the response of y is "4". 4 % again? | |
| Mar 23, 2016 at 18:12 | comment | added | Sympa | Arvo, your variables have to be on the same basis. So a 1% unexpected increase in X causes a 4% increase in Y. If you use full units of percentage points: an unexpected increase in X by 1 full unit causes a 4 unit increase in Y. The VAR IRFs estimation does all the calculation for you going forward how many periods you decide to go. At least, that is the case using the vars package in VAR and using the irf() function. | |
| Mar 23, 2016 at 16:20 | comment | added | Arvo P. | Okay, thanks! To clarify this further, let's say that first I had my data (both x and y, naturally) in the notation of "0,01" (i.e. percentages in their "raw numerical format"). Then the impulse response for lag 1 was 0,04. I interpreted this as "one unit, i.e. 100%, unexpected increase in x" leads to 0,04 (4%) increase in y after the lag of one. Okay. Then I multiplied my data, both x and y by 100. Now I have 1% as "1". Then I repeat the VAR and the impulse response estimation. I end up getting 4 as the same impulse response. I interpret this as 1% increase in x --> 4 % increase in y. Err? | |
| Mar 22, 2016 at 22:27 | history | answered | Sympa | CC BY-SA 3.0 |