This might be a really basic question for some of you but I have been looking up how to interpret impulse responses but most of the answers that were presented did not quantify the responses but rather explained how the variables responded to structural shocks.
In my model, I want to interpret the shocks in Oil prices to Real GDP. Due to the series being integrated of order (I), I logged these variables and then first differenced them. However, after generating the impulse response functions, I am having trouble with the interpretation and whether or not the result is significant. For this example, I want to examine the impact of an oil price shock to real GDP? Does it mean, a 1% point increase in oil prices leads to _ _ _ % point increase/decrease in GDP?
Code:
quietly var Dlprod Dlrea Dlop Dlrgdp Dinf Dlexc, lags(1) irf create Dvar1, set (Dvar1) step(8) irf graph oirf, impulse(Dlop) response(Dlrgdp)
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(quarter Dlprod Dlrea Dlop Dlrgdp Dinf Dlexc) 80 . . . . . . 81 -3.5614014 21.27188 -.366044 -4.316902 2.503651 -.08628368 82 -1.580906 -9.3294115 -8.054447 -2.895641 2.0455003 -2.0701766 83 -3.905678 11.385426 17.828773 8.399487 .8228207 3.7534475 84 2.488899 -3.9214146 -4.7052145 -1.137352 1.62337 6.241119 85 -2.1674156 -17.736504 -10.607576 -2.2613525 -.7381243 6.047547 86 -4.679394 -20.63615 -5.337715 -1.5737534 -.4310148 6.826341 87 .1235962 -7.688273 5.288005 5.860233 -1.1944033 -4.1271687 88 -1.5400887 -12.69482 -8.884788 -1.995945 -.48498535 1.9032836 89 -2.8642654 .7563127 4.5670033 -2.524471 -.8472849 2.091241 90 2.0044327 -37.88605 -.9387732 -.9765625 -.19893816 8.882523 91 2.977276 13.620292 -.6835461 6.259918 .7637694 7.321393 92 -7.004642 10.261118 -8.00426 -.9399414 -1.834847 -.5073905 93 2.050209 1.1093835 1.0914087 .7008553 -.6644893 .8375525 94 4.874039 -20.986546 2.732563 -1.7912865 -1.3179516 3.2039404 95 .1842499 6.094514 -4.5812845 6.514645 -.5009662 1.3346195 96 -.27475357 3.264539 1.0084867 .942421 -.979468 2.453232 97 .5511284 5.624531 .27327538 -2.0303726 .16417363 .6761789 98 -1.7630577 -9.404474 -4.0613413 2.0859718 -.3391408 7.764363 99 .28762817 7.612477 -2.3606777 4.980183 -.1692508 5.618048 100 -.3787041 -5.470981 -.4496574 1.1543274 -.27414608 5.679584 101 -2.3112297 -1.1720886 -1.5342474 -3.608227 .06894309 -5.182862 102 .2131462 -25.89348 .5304575 1.8979073 .019889154 -6.399655 103 6.078339 16.199266 3.5532236 6.870365 -.1504912 -6.899428 104 -1.2742996 -11.91052 -49.2239 -2.629471 .1398328 -6.177926 105 1.4677048 -17.468552 -32.13961 -.57058334 .28220645 1.1311531 106 2.0018578 -5.437137 0 -.07314682 1.9575448 -.21862984 107 -2.536392 17.804308 16.257263 7.444286 .7993081 1.1133432 108 -1.9616127 12.202985 19.018364 -4.241371 1.1553462 -5.986404 109 .6961823 15.999536 3.786588 .3426552 -.2632084 -4.7186255 110 5.217457 .171016 2.946901 -3.904152 -1.80022 .28271675 111 -.3912926 13.320133 -6.451511 9.070873 -.4852419 -3.897548 112 -1.266098 23.66103 -11.970901 -3.8051605 -.3382464 -1.6815186 113 .48418045 -8.147742 2.114606 -3.488922 -.05461295 -1.7273188 114 1.7139435 -12.503026 -13.296938 -.9062767 -.4068208 8.864713 115 4.4962883 14.290818 -6.906033 6.791592 -.5988197 -3.344822 116 -4.605198 10.090512 25.841665 -5.028057 -1.3222104 1.683426 117 .8478165 -1.9887235 8.156276 2.2555351 -.016248424 4.1544795 118 1.911831 -11.435586 -5.910301 -2.4207115 -.159935 .4276037 119 2.1557808 11.969423 7.254767 7.222748 -.21699703 -2.7472496 120 -.12311935 -.435745 3.893995 -2.434349 .064706445 -4.6969533 121 -.3965378 -20.244116 -20.725727 -2.5891304 -.5352536 -.6798387 122 -3.584957 -13.706973 48.35284 -1.1367798 -.026493566 -5.305231 123 2.464962 11.673787 18.918371 8.31728 .7205932 -4.859436 124 .0743866 17.598938 -44.57832 -3.19252 -.624119 1.8657684 125 -2.3163795 4.360292 -7.966495 1.3855934 -.09950262 12.299943 126 1.5841484 -8.160953 5.793595 -2.881718 -.3029495 .8721352 127 1.1842728 3.26201 2.2568226 6.528664 -.9227142 -6.388617 128 -.5056381 -17.664976 -12.64615 .262928 -.235843 -.5768895 129 -1.706028 -10.726147 11.71906 -3.0332565 .08376388 -.6651998 130 .55150986 -12.648213 1.5793324 .8387566 -.11639638 -8.576798 131 1.4642715 12.127905 -5.341005 5.217743 -.06327721 9.891403 132 -.08001328 11.635007 -5.233955 -3.020191 .3614076 8.356285 133 -1.7170906 13.089548 .018525124 -1.2756348 -.1864905 -1.551175 134 .6093025 -10.519378 -10.499 1.605034 -.26853305 5.739963 135 .9690285 -7.333334 -7.940865 9.344959 -.16460283 1.0073781 136 .7410049 -10.070403 -7.587433 -4.366684 -.7418476 1.5948415 137 -.29649734 15.256085 15.959454 .847435 -.26816398 -3.184819 138 .15182495 6.766533 4.6827316 -2.486992 .5646817 -5.193007 139 1.778221 24.672945 -2.654672 9.761047 .2017622 -1.6186953 140 -.10061264 8.848517 3.6661625 -2.917862 .9194045 -3.2115936 141 .3125191 4.494567 5.387354 -3.1635284 .033250786 -4.4647813 142 .9683609 -6.959984 -10.04212 2.602291 -.3688772 1.0604978 143 .3537178 -19.592417 3.2122374 7.105446 -.1604253 -.3875852 144 .7936478 -13.858634 7.578969 -1.1328697 -1.267848 2.1550179 145 -.010108948 -5.717646 6.210089 -3.0052185 .09279093 1.8499613 146 .338459 -24.72658 5.70159 2.9580116 .3933002 -1.876986 147 1.6378403 21.819086 11.149788 4.030895 .3908738 .3127813 148 1.0835648 7.81161 -8.976126 -1.7730713 1.288968 2.99412 149 -.0919342 -10.347562 -13.125277 3.354168 -.3671627 6.529093 150 .6411552 .016839333 .897336 -2.5523186 -.4056301 5.278087 151 1.3442993 -3.587861 .9778738 7.63483 -.06284544 -4.578638 152 1.8574715 -17.088844 -28.53258 -1.279831 -.07036417 5.636358 153 -.7968903 -10.757398 -6.439734 -2.697754 .08619802 -.3437281 154 -1.6814232 -13.08358 -2.1053314 -2.005291 .04336648 1.630497 155 .5273819 10.483965 -9.2877865 6.525898 .03437156 -1.9602537 156 .7332802 -6.893462 -1.75941 -2.033615 -.06871117 2.2954226 157 -3.0688286 13.174582 31.97281 -2.3492813 .22185943 1.7477036 158 1.0526657 5.977685 24.31948 1.3630867 -.42495185 .5753994 159 .3252983 23.65044 15.230894 7.452679 .635814 .6227255 160 1.356125 7.981205 11.15718 -.06017685 .20604932 4.070139 161 1.726532 10.235437 .56197643 -6.03199 .02448303 6.567001 162 1.7752647 1.8265775 11.014366 .8555412 .5073769 1.9916296 163 .9736061 1.66046 -.7164478 6.538296 -.3121837 3.298044 164 -1.1986732 -11.228832 -12.935114 -.5785942 .4127289 -4.0249586 165 -2.3813248 -6.635313 2.4876595 -4.5614243 .4161568 3.201628 166 .9342194 -31.22 -5.828905 1.0370255 -1.38022 -2.0018816 167 -.3803253 -16.602625 -26.657724 6.894302 -.54820776 -1.0724306 168 -1.3516426 11.97133 8.00693 -4.633999 -.9773316 .09560585 169 -.4775047 4.733926 18.611122 2.1159172 -.56555325 -8.499837 170 .8533478 1.797579 6.651235 -3.976345 .9514818 -8.406281 171 1.8037796 34.65791 -.7452488 7.687569 .8089243 -2.767825 172 .8569717 14.711061 15.875292 -2.139473 2.376654 -3.637564 173 -.6990433 21.599167 -16.814756 -4.574585 -2.358829 -.6904006 174 .8543015 7.306918 6.91607 1.1060715 -.3375081 4.578185 175 2.9878616 63.04437 3.383112 6.490231 -.6932737 -5.8622 176 .9285927 19.107704 9.015703 1.4725685 -2.642757 -.1775503 177 .26216507 -38.24028 10.330415 -3.930473 2.2560117 -.6779671 178 1.3513565 10.88041 12.952352 -1.8507004 .3781398 .04857779 179 .5133629 23.166685 5.228329 8.005524 .035423394 -8.03665 end format %tq quarter
Thank you.
0 Response to Structural VAR with Orthogonalised Impulse Response Functions
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