I'm relatively new to the VAR model and have been using Sean Becketti's 'Introduction to Time Series Using State' as reference and wanted to check if I am on the right track.
As of now, I have 5 variables in the VAR Model. [GDP, Oil prices, Exchanged rates (Expressed in Logs)] , [Inflation Rate and Unemployment Rate (Expressed in percentages)]
I performed the Unit Root tests and all of them proved to be non-stationary and did the Johansen Cointegration tests using
vecrank lrgdp lop lexc inf unp, max ic
However,
the variables contain a unit root, I don't know if I should estimate the VAR using the variables in their first differences or in levels.
I did it in levels
varbasic lrgdp lop lexc inf unp, lags (1 2 3 4 5)
varbasic dlrgdp dlop dlexc dinf dunp, lags (1 2 3 4 5)
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(lrgdp lop lexc) double(unp inf) float(Dlrgdp Dlop Dlexc Dunp Dinf) 12.733817 3.571784 1.598734 1.5 7.74278215223094 . . . . . 12.690648 3.568123 1.5978713 1.4 10.2464332036316 -.04316902 -.0036604404 -.0008628368 -.1 2.503651 12.661692 3.487579 1.5771695 2.1 12.291933418694 -.028956413 -.08054447 -.020701766 .7 2.0455003 12.745687 3.6658666 1.614704 1.6 13.1147540983606 .08399487 .17828774 .03753448 -.5 .8228207 12.734313 3.6188145 1.6771152 1.8 14.7381242387332 -.01137352 -.04705215 .06241119 .2 1.62337 12.7117 3.512739 1.7375907 2 14 -.022613525 -.10607576 .06047547 .2 -.7381243 12.695962 3.4593616 1.805854 2.4 13.5689851767389 -.015737534 -.05337715 .06826341 .4 -.4310148 12.754564 3.5122416 1.7645824 1.8 12.3745819397994 .05860233 .05288005 -.04127169 -.6 -1.1944033 12.734605 3.423394 1.7836152 2 11.8895966029724 -.01995945 -.08884788 .019032836 .2 -.48498535 12.70936 3.469064 1.8045276 2.4 11.0423116615067 -.025244713 .04567003 .02091241 .4 -.8472849 12.699594 3.459676 1.893353 2.8 10.8433734939759 -.009765625 -.009387732 .08882523 .4 -.19893816 12.762194 3.4528406 1.9665668 3.2 11.6071428571428 .06259918 -.00683546 .073213935 .4 .7637694 12.752794 3.372798 1.961493 3.6 9.77229601518024 -.009399414 -.0800426 -.005073905 .4 -1.834847 12.759803 3.383712 1.9698684 3.7 9.1078066914498 .007008553 .010914087 .0083755255 .1 -.6644893 12.74189 3.411038 2.0019078 3.8 7.78985507246374 -.017912865 .02732563 .032039404 .1 -1.3179516 12.807036 3.365225 2.015254 2.6 7.28888888888891 .065146446 -.04581285 .013346195 -1.2 -.5009662 12.81646 3.37531 2.0397863 3.2 6.30942091616251 .00942421 .010084867 .02453232 .6 -.979468 12.796157 3.3780425 2.0465481 3.3 6.47359454855195 -.020303726 .002732754 .00676179 .1 .16417363 12.817017 3.337429 2.1241918 3.6 6.13445378151261 .02085972 -.04061341 .07764363 .3 -.3391408 12.866818 3.313822 2.1803722 2.5 5.96520298260146 .04980183 -.023606777 .05618048 -1.1 -.1692508 12.878362 3.309326 2.237168 3 5.69105691056911 .011543274 -.0044965744 .05679584 .5 -.27414608 12.84228 3.293983 2.1853395 2.3 5.75999999999999 -.036082268 -.015342474 -.05182862 -.7 .06894309 12.861259 3.299288 2.121343 3.1 5.77988915281077 .01897907 .005304575 -.06399655 .8 .019889154 12.929962 3.33482 2.0523486 2 5.62939796716187 .06870365 .035532236 -.06899428 -1.1 -.1504912 12.903667 2.842581 1.9905694 2 5.76923076923076 -.02629471 -.492239 -.06177926 0 .1398328 12.897962 2.521185 2.001881 1.9 6.05143721633889 -.005705833 -.3213961 .01131153 -.1 .28220645 12.89723 2.521185 1.9996946 2.3 8.00898203592812 -.0007314682 0 -.0021862984 .4 1.9575448 12.971673 2.6837575 2.010828 1.7 8.80829015544041 .07444286 .1625726 .011133432 -.6 .7993081 12.92926 2.873941 1.950964 2.4 9.96363636363637 -.04241371 .19018364 -.05986404 .7 1.1553462 12.932686 2.911807 1.9037777 1.8 9.70042796005709 .003426552 .03786588 -.04718626 -.6 -.2632084 12.893644 2.941276 1.906605 2.2 7.90020790020794 -.03904152 .029469013 .0028271675 .4 -1.80022 12.984353 2.876761 1.8676294 1.9 7.41496598639454 .09070873 -.064515114 -.03897548 -.3 -.4852419 12.946301 2.757052 1.8508142 2.5 7.07671957671957 -.0380516 -.11970901 -.016815186 .6 -.3382464 12.911412 2.778198 1.833541 2.9 7.02210663198959 -.03488922 .02114606 -.017273188 .4 -.05461295 12.90235 2.6452286 1.922188 3.4 6.61528580603723 -.009062767 -.13296938 .08864713 .5 -.4068208 12.970265 2.576168 1.88874 3.9 6.01646611779607 .06791592 -.069060326 -.03344822 .5 -.5988197 12.919985 2.834585 1.9055742 4.8 4.69425571340333 -.05028057 .25841665 .016834259 .9 -1.3222104 12.94254 2.916148 1.947119 4.76666666666667 4.67800729040098 .02255535 .08156276 .0415448 -.033333335 -.016248424 12.918333 2.857045 1.951395 5.03333333333333 4.51807228915663 -.024207115 -.05910301 .004276037 .26666668 -.159935 12.99056 2.9295924 1.9239225 4.93333333333333 4.30107526881723 .07222748 .072547674 -.027472496 -.1 -.21699703 12.966217 2.968532 1.876953 5.66666666666667 4.36578171091446 -.02434349 .03893995 -.04696953 .7333333 .064706445 12.940326 2.761275 1.8701546 5.16666666666667 3.83052814857803 -.025891304 -.20725727 -.006798387 -.5 -.5352536 12.928958 3.2448034 1.8171023 5.2 3.80403458213255 -.011367798 .4835284 -.05305231 .033333335 -.026493566 13.01213 3.433987 1.768508 4.83333333333333 4.524627720504 .0831728 .1891837 -.04859436 -.3666667 .7205932 12.980206 2.988204 1.7871656 5.6 3.90050876201245 -.0319252 -.4457831 .018657684 .7666667 -.624119 12.994061 2.908539 1.910165 5.23333333333333 3.80100614868644 .013855934 -.07966495 .12299943 -.3666667 -.09950262 12.965244 2.966475 1.9188864 5.46666666666667 3.49805663520268 -.028817177 .05793595 .008721352 .23333333 -.3029495 13.03053 2.989043 1.8550003 5.5 2.57534246575342 .06528664 .022568226 -.063886166 .033333335 -.9227142 13.03316 2.862582 1.8492314 6.1 2.33949945593033 .00262928 -.1264615 -.005768895 .6 -.235843 13.002828 2.979772 1.8425794 5.96666666666667 2.4232633279483 -.030332565 .1171906 -.006651998 -.13333334 .08376388 13.011215 2.995566 1.7568114 6 2.30686695278971 .008387566 .015793324 -.08576798 .033333335 -.11639638 13.063393 2.9421556 1.8557254 5.63333333333333 2.24358974358973 .05217743 -.05341005 .09891403 -.3666667 -.06327721 13.03319 2.889816 1.9392883 6.26666666666667 2.60499734183944 -.03020191 -.05233955 .08356285 .6333333 .3614076 13.020434 2.890001 1.9237765 6.1 2.41850683491062 -.012756348 .00018525124 -.01551175 -.16666667 -.1864905 13.036485 2.785011 1.981176 6.1 2.14997378080752 .016050339 -.10499 .05739963 0 -.26853305 13.129934 2.705603 1.99125 5.33333333333333 1.98537095088822 .0934496 -.07940865 .01007378 -.7666667 -.16460283 13.086267 2.629728 2.0071983 5.63333333333333 1.2435233160622 -.04366684 -.07587433 .015948415 .3 -.7418476 13.094742 2.789323 1.97535 5.66666666666667 .975359342915802 .00847435 .15959454 -.031848192 .033333335 -.26816398 13.069872 2.83615 1.92342 5.36666666666667 1.54004106776183 -.02486992 .04682732 -.05193007 -.3 .5646817 13.167482 2.8096035 1.907233 4.96666666666667 1.74180327868853 .09761047 -.026546717 -.016186953 -.4 .2017622 13.138304 2.846265 1.8751172 5.53333333333333 2.66120777891503 -.02917862 .036661625 -.032115936 .56666666 .9194045 13.106668 2.9001386 1.8304694 5.2 2.69445856634468 -.031635284 .05387354 -.04464781 -.3333333 .033250786 13.13269 2.7997174 1.8410743 4.8 2.3255813953488 .02602291 -.1004212 .010604978 -.4 -.3688772 13.203746 2.83184 1.8371985 4.2 2.16515609264855 .07105446 .032122374 -.0038758516 -.6 -.1604253 13.192417 2.9076295 1.8587487 5 .897308075772663 -.011328697 .0757897 .02155018 .8 -1.267848 13.162365 2.9697304 1.8772483 5 .990099009900991 -.030052185 .06210089 .018499613 0 .09279093 13.191945 3.026746 1.8584784 4.76666666666667 1.38339920948619 .029580116 .0570159 -.01876986 -.23333333 .3933002 13.232254 3.138244 1.8616062 4.23333333333333 1.77427304090683 .04030895 .11149788 .003127813 -.53333336 .3908738 13.214523 3.048483 1.8915474 4.26666666666667 3.06324110671939 -.017730713 -.08976126 .0299412 .033333335 1.288968 13.248065 2.91723 1.9568384 4.23333333333333 2.69607843137256 .03354168 -.13125277 .06529093 -.033333335 -.3671627 13.222542 2.9262035 2.0096192 3.96666666666667 2.29044834307992 -.025523186 .00897336 .05278087 -.26666668 -.4056301 13.29889 2.935982 1.963833 3.2 2.22760290556901 .0763483 .009778738 -.04578638 -.7666667 -.06284544 13.286092 2.6506565 2.0201964 3.3 2.15723873441995 -.01279831 -.28532577 .05636358 .1 -.07036417 13.259114 2.586259 2.0167592 3.46666666666667 2.2434367541766 -.02697754 -.064397335 -.003437281 .16666667 .08619802 13.23906 2.565206 2.0330641 3.1 2.28680323963794 -.02005291 -.021053314 .01630497 -.3666667 .04336648 13.30432 2.472328 2.0134616 2.5 2.32117479867363 .06525898 -.09287786 -.019602537 -.6 .03437156 13.283984 2.454734 2.0364158 2.8 2.25246363209761 -.02033615 -.017594099 .022954226 .3 -.06871117 13.26049 2.774462 2.0538929 2.96666666666667 2.47432306255837 -.023492813 .3197281 .017477036 .16666667 .22185943 13.274122 3.017657 2.0596468 3.2 2.04937121564973 .013630867 .2431948 .005753994 .23333333 -.42495185 13.34865 3.169966 2.065874 3.16666666666667 2.68518518518515 .07452679 .15230894 .006227255 -.033333335 .635814 13.348047 3.2815375 2.1065755 3.4 2.89123451124368 -.0006017685 .11157179 .04070139 .23333333 .20604932 13.287727 3.287157 2.1722455 3.2 2.91571753986331 -.0603199 .005619764 .06567001 -.2 .02448303 13.296283 3.397301 2.1921618 3.33333333333333 3.42309447740759 .008555412 .11014366 .019916296 .13333334 .5073769 13.361666 3.3901365 2.2251422 3.03333333333333 3.11091073038775 .06538296 -.007164478 .032980442 -.3 -.3121837 13.35588 3.260785 2.1848927 3.33333333333333 3.52363960749332 -.005785942 -.12935114 -.04024959 .3 .4127289 13.310266 3.285662 2.216909 3.36666666666667 3.93979637007527 -.04561424 .024876595 .032016277 .033333335 .4161568 13.320636 3.227373 2.19689 3.56666666666667 2.5595763459841 .010370255 -.05828905 -.020018816 .2 -1.38022 13.38958 2.9607956 2.1861658 3.36666666666667 2.0113686051596 .06894302 -.26657724 -.010724306 -.2 -.54820776 13.34324 3.040865 2.1871219 3.73333333333333 1.0340370529944 -.04633999 .0800693 .0009560585 .3666667 -.9773316 13.364398 3.226976 2.1021235 3.73333333333333 .468483816013621 .02115917 .1861112 -.08499837 0 -.56555325 13.324635 3.2934885 2.0180607 3.66666666666667 1.41996557659209 -.03976345 .066512346 -.08406281 -.06666667 .9514818 13.40151 3.286036 1.9903824 3.53333333333333 2.22888984140592 .07687569 -.007452488 -.02767825 -.13333334 .8089243 13.380116 3.444789 1.9540068 3.96666666666667 4.60554371002131 -.02139473 .1587529 -.03637564 .4333333 2.376654 13.33437 3.2766414 1.9471028 4.33333333333333 2.24671470962272 -.04574585 -.16814756 -.006904006 .3666667 -2.358829 13.34543 3.345802 1.9928846 4.43333333333333 1.90920661858297 .011060715 .0691607 .04578185 .1 -.3375081 13.410333 3.379633 1.9342626 4.1 1.21593291404613 .064902306 .03383112 -.058622 -.3333333 -.6932737 13.425058 3.46979 1.932487 4.13333333333333 -1.42682429677945 .014725685 .09015703 -.001775503 .033333335 -2.642757 13.385754 3.5730944 1.9257075 4.33333333333333 .829187396351576 -.03930473 .10330415 -.006779671 .2 2.2560117 13.367247 3.702618 1.9261932 4.5 1.20732722731057 -.018507004 .12952352 .00048577785 .16666667 .3781398 13.447302 3.754901 1.8458267 4.13333333333333 1.24275062137531 .08005524 .05228329 -.0803665 -.3666667 .035423394 end
0 Response to Estimating a reduced form VAR Model
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