Hi,
I am trying to bootstrap standard error for the following VAR model:
But I am getting the following error:
I cannot increase the number of observations in my sample but I am willing to use whatever option bootstrap offers to attempt to get it to run. Would you be able to help me with that?
As a side note, I am trying to reduce the confidence interval (increase statistical significance) that I then exhibit in a "irf graph". While I would really like to get some suggestions to get this bootstrap method to work, I would also welcome other alternative methods implemented in Stata, like Monte Carlo simulations that may also suit my purpose. Your help is greatly appreciated!
I am trying to bootstrap standard error for the following VAR model:
Code:
// Set the number of bootstrap replications, e.g., 1000 for a thorough analysis set seed 123456 // Set a seed for reproducibility local numreps = 1000 // Bootstrap the VAR model with the chosen number of lags, here assumed to be based on prior selection bs, reps(`numreps'): var yoy_ngdp yoy_unemp yoy_cpi yoy_int, lags(1/4) exog(aaci_combined_au) dfk small
Code:
. // Bootstrap the VAR model with the chosen number of lags, here assumed to be based on prior selection . bs, reps(`numreps'): var yoy_ngdp yoy_unemp yoy_cpi yoy_int, lags(1/4) exog(aaci_combined_au) dfk small (running var on estimation sample) Bootstrap replications (1000) ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 50 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 100 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 150 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 200 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 250 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 300 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 350 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 400 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 450 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 500 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 550 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 600 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 650 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 700 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 750 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 800 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 850 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 900 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 950 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1000 insufficient observations to compute bootstrap standard errors no results will be saved r(2000);
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double(qdate yoy_ngdp yoy_unemp yoy_cpi yoy_int aaci_combined_au) 84 .1271122025375666 -.04587305457845059 .09448818898639155 .28643852946768056 -.36 85 .14292247884747322 -.11417189168872977 .08396946555473894 .18588177767809433 .03 86 .1664775714641502 -.04786934240414553 .09022556393716652 .259419896920732 -.12 87 .14697246250297602 -.0017045554886018222 .11029411798761823 .2836290780527677 .1 88 .14841520983555534 .07610175352398363 .10791366897836752 .2862068971148293 .1 89 .1497348939575831 .18833087161355144 .109154929550765 .2030895986000183 -.42 90 .10397836452921494 .22136159380395437 .12413793088340497 .07447266442650191 -.63 91 .09045411563919825 .4726505233415663 .11258278119958387 -.07558528428093636 -.47 92 .07681362363357125 .5441401975820732 .11363636343773043 -.24013787816159748 -.27 93 .04890039372647981 .5593660611895279 .11111111092120818 -.30987844530046227 .32 94 .09601168987450581 .45398618620312803 .09202454016301287 -.27844551262019235 -.01 95 .10841564264715209 .10360811133060843 .08630952371700973 -.26724553762662806 .22 96 .14497556473771422 -.029640802187959836 .05830903807842769 -.10357862900615467 -.02 97 .15657728281382854 -.1055894916588288 .040000000246114276 .003969238153319621 -.22 98 .10673672653125243 -.14843044977368225 .03651685382468495 -.04525263796633738 -.1 99 .09510179937794572 -.11021638326690542 .024657534293452077 .1445029620985836 -.22 100 .09075088387770425 -.08922545135089699 .04407713499439758 .19342142221157688 -.23 101 .1092721762986606 -.07400158087246089 .0659340659438179 .16308376575240913 .09 102 .12177579365079372 -.07472216214081873 .07588075868106126 .40069787798798995 -.08 103 .12512414838563535 -.08831315040066778 .08288770052860106 .5559390276100087 -.27 104 .1047982130561107 -.07173066663208816 .09234828479925561 .26501766784452285 -.2 105 .08240790133035891 -.07641221091942596 .08505154616073018 -.04036541300191199 -.01 106 .07709800701178104 .008596162485175585 .08816120920259629 .044218393181603144 -.22 107 .09177352412810502 .06278495056335132 .09629629629826564 -.1081330868961431 -.36 108 .09852089445601453 .04306352193279417 .0942028985488399 -.08808193687150823 -.27 109 .11978614417347422 .049327676200307646 .09263657957305416 -.05025459396717846 -.2 110 .13494809688581322 -.029157823796887472 .08333333322948011 -.2598409544215028 -.23 111 .13108173281511526 -.047881090140257876 .0720720720982897 -.2559585488613385 .04 112 .1433770618842023 -.0918952068593929 .06843267107433171 -.3271390648003143 -.15 113 .12908431356858974 -.06370692053375704 .07173913048923075 -.14289044265734274 .15 114 .13282864820173623 -.13122023226947577 .07264957273216344 .07547676631748579 .16 115 .13522868358018036 -.14799625780750614 .0756302522396064 .22116991581583 .46 116 .12793948562783664 -.12259440896933127 .06818181823237945 .4977238244302651 -.11 117 .14271033888094875 -.19521270449218286 .0750507098797839 .4509110681395403 .36 118 .12422169004447481 -.13338324625769826 .07968127492176214 .3521478520599033 -.48 119 .1082720222708291 -.13236133252737992 .07812500001642997 .24315693481823852 -.09 120 .09629168633846952 -.05363662922147605 .08704061890233761 .00040526869300916424 -.12 121 .07615021241846964 .0489915903713507 .07735849061893618 -.15257731942878683 .41 122 .049744885020744034 .20108426900895426 .060885608896041 -.21924639837102955 .16 123 .044157371689443004 .34252107343870386 .0688405796748075 -.3088073393928794 -.28 124 .011412351962415634 .39837973413823113 .04804270471428973 -.29025724138337916 .37 125 -.006675631543559657 .48770199797392655 .03327495629807409 -.2817960630391506 -.27 126 .0069960478127688575 .37217399887231495 .031304347692236334 -.2890939198012775 .28 127 .004257634626215978 .2946931558004373 .015254237314427987 -.33235996834819226 -.43 128 .03286366803437324 .19388268644277362 .016977928742669457 -.3584474886130137 -.16 129 .03588454286128706 .10645282604183759 .011864406759555024 -.37911918675057577 -.36 130 .037894272197752654 .09766768878543841 .008431703320403328 -.42396006674743425 .02 131 .04872669892299353 .08661992457273793 .0033388980294626336 -.298608349872708 -.15 132 .051268597983735065 .04751615825571509 .011686143552514405 -.24021352314235822 -.16 133 .05786643765285748 .01998859494592997 .01842546070872042 -.2242063492559524 -.4 134 .04669933431952655 .006354395776418276 .02173913037215347 -.1513575967071057 -.25 135 .04784953546340365 -.024735435836105824 .01830282869230171 -.18310657602040814 -.2 136 .05080032797404721 -.039297709395872515 .014851485174023571 -.15163934421262526 -.05 137 .05414515773218609 -.08749274896054615 .018092105155676164 -.04795396419743947 .15 138 .08632553373788765 -.12962653947392955 .019639934627289746 .15316541864171773 -.22 139 .057878620116950996 -.16774726031091491 .026143790856572702 .49618320628009593 -.11 140 .0560710408467906 -.158582016167651 .037398373969220966 .6963423049689441 -.16 141 .05600991118440324 -.15214847296579725 .04523424888812966 .5540631296544032 -.23 142 .04760781890327204 -.1188577352882787 .05136436598109473 .33589138120803463 .37 143 .07442659143873431 -.07054574201040642 .050955414025843915 .03710575129698013 -.08 144 .06893948520258597 -.043214852444324436 .03761755486687002 -.08624898283507931 .05 145 .07156446191895482 .00550299587377534 .030911901004493147 -.01944684529038221 -.22 146 .05489797343971925 .028761765842313602 .021374045764382288 -.05479452050617739 .07 147 .04916398395701571 .030522207800008472 .015151515196009102 -.13774597487199214 .09 148 .04502663430979936 .027014775993800644 .013595166023586458 -.20837043632197816 .06 149 .055030329930463084 .007213237073596668 .002998500790769576 -.23931247246537302 -.25 150 .05656601330272526 -.027010274667525813 -.004484304994083699 -.30528284249649373 -.48 151 .06935447601500111 -.07295929595900219 -.00298507466764264 -.2297717842723147 -.29 152 .06725764242371945 -.09090802946780652 -.0014903128258202392 -.1608548932417967 -.33 153 .04923607653959139 -.09038495131619428 .007473841495857059 -.11935110070216404 .33 154 .060480501392757624 -.06777526153967384 .013513513537546151 .035666218104688285 .05 155 .052300261840480866 -.07372613986982579 .014970059924156676 -.022895622828282836 .25 156 .05439651862280814 -.09558167411202945 .011940298509861336 -.0368632707129265 -.08 157 .05206460796172174 -.11153833792696566 .01038575665949204 -.042763157957713016 .21 158 .05322914272581025 -.11552597269191445 .017777777702626674 -.03703703703703698 -.01 159 .05209246557681402 -.09847773434864682 .0191740412421868 .13025499654512362 .04 160 .07444320926131787 -.061858930860465566 .028023598726364174 .2073764787056367 .03 161 .08116520148381534 -.08578190654272899 .03083700451324889 .2769759449484537 -.1 162 .0875485532230611 -.12594167532936895 .06113537123858381 .2995951417004048 -.05 163 .06303573945064311 -.07083551173744962 .05788712013043207 .15487804864909283 -.05 164 .06122825169170021 -.04229269252364909 .060258249697394906 -.040922190088813926 .39 165 .05878620527583567 .08380293135936712 .06125356116944092 -.20775026901010507 -.09 166 .05487493765514562 .14072660166780038 .024691358068455127 -.24506749735202493 .24 167 .07561514031942917 .13057264482414754 .03146374827913245 -.3194297781583648 -.09 168 .0686105865939004 .04919509523164378 .02976995937522342 -.21694711549176993 .13 169 .07641385892484576 -.06373207096616629 .02818791941652732 -.014945652308767321 -.02 170 .06959050025814517 -.08336957284887525 .03212851406375039 .02200825295584541 .08 171 .07212672010974397 -.11816306258065568 .029177718804481723 .13033359192161864 .1 172 .06051930619350232 -.08816252535825309 .03285151119591556 .0982348427550448 -.03 173 .052781887446065934 -.05396993514718784 .02610966065295428 -.017931034346064023 .17 174 .062136070321630044 -.06126071548501888 .02594033716044275 -.027590847781129968 .14 175 .07109798276654877 -.06929312403723131 .024484536141302637 .0809883321152376 .13 176 .07984356613829857 -.0881718977553021 .020356234101572168 .16212438846960175 .17 177 .0853564188692919 -.10113801849784365 .025445292558469834 .16151685378079228 -.11 178 .07599060462401752 -.07074354541362715 .02275600509750375 .12941176462772241 -.12 179 .06762450945165921 -.10034490473695068 .02515723264221914 .031746031809523956 .07 180 .06658669364992886 -.07878125223092214 .023690772988986586 .013830427000230516 .02 181 .07279231969971756 -.06862958340202652 .02481389585589211 .033252720800075686 .12 182 .08278796393448462 -.09341285510239528 .0309023486028408 .03492647058823528 .42 183 .08709619612488151 -.018174105475708302 .028220858887113787 .03938461532065318 .21 end format %tq qdate
As a side note, I am trying to reduce the confidence interval (increase statistical significance) that I then exhibit in a "irf graph". While I would really like to get some suggestions to get this bootstrap method to work, I would also welcome other alternative methods implemented in Stata, like Monte Carlo simulations that may also suit my purpose. Your help is greatly appreciated!
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