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karen8169
Obsidian | Level 7
Data paper;
input date search_volume AvgPrice_change volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
datalines;
20131229 31265.96873 -8049968 101.6245784 85.46101172 100.6374401 115.9752537 118.0235693 93.97899654
20140105 27950.70473 -32.37 16673309 101.4670963 85.20483674 99.47578826 116.2290342 117.99756 93.82381802
20140112 25902.91541 34.49 -1744281 101.3045585 84.9599483 98.31558748 116.4796395 117.9683595 93.67532876
20140119 23850.62263 8.1 -2784694 101.1367771 84.74080987 97.1608941 116.7261614 117.9323331 93.54195118
20140126 16471.45342 -76.11 -17009604 100.9635581 84.57583371 96.02679831 116.9660368 117.8800425 93.44019161
20140202 18413.8849 -229.31 9574282 100.7847013 84.52277179 94.96965667 117.1940095 117.7882222 93.40347039
20140209 22096.8015 126.25 7354967 100.6 84.7 94.2 117.4 117.6 93.5
20140216 21174.52948 121.41 -35509 101.0706661 85.3506424 94.50432279 117.5650212 117.1765452 93.86927514
20140223 19110.34379 65.78 -2446057 101.5358183 86.23143162 95.09613037 117.707928 116.6566982 94.37169343
20140302 20039.02689 23.54 5415362 101.9959941 87.22377081 95.76489134 117.8386143 116.0973493 94.93887711
20140309 20082.52542 79.28 -2237921 102.4517257 88.26948972 96.45424708 117.9620219 115.5218055 95.53709453
20140316 18755.35847 -9.17 759321 102.9035397 89.33933393 97.14909964 118.080143 114.9396057 96.14921551
20140323 18181.1214 -156.99 -386470 103.3519585 90.41711766 97.84536379 118.1928278 114.3546296 96.76554749
20140330 16583.05049 262.66 -3366550 103.7975004 91.490956 98.54178229 118.2977235 113.768397 97.37865063
20140406 17277.2075 41.98 5017880 104.2406805 92.54682679 99.23740718 118.3893211 113.1813592 97.9793578
20140413 16706.70072 52.32 -1804624 104.6820111 93.56097486 99.92970051 118.4567229 112.593283 98.55187068
20140420 16448.80356 64.29 3505396 105.1220022 94.48704387 100.6094497 118.4792265 112.0030525 99.06530847
20140427 15159.39418 -281.38 -6300657 105.5611627 95.23096501 101.2423105 118.4179359 111.4077198 99.45718826
20140504 16407.68482 215.73 5698531 106 95.6 101.7 118.2 110.8 99.6
20140511 15863.96562 -2.15 -3449302 105.2148274 95.19833474 101.5032869 117.689092 110.1620765 99.23299517
20140518 14733.00752 7.82 -15661 104.4289314 94.42165813 101.131402 117.0215782 109.5117766 98.61684747
20140525 14988.46355 138.95 3910103 103.6414034 93.46251545 100.7126266 116.2703653 108.8564055 97.87895215
20140601 13836.44918 66.12 -3898532 102.8513332 92.4146101 100.2812996 115.4744435 108.1989573 97.08157585
20140608 14721.77203 47.32 4140873 102.0578073 91.32356274 99.84661271 114.6546858 107.5406598 96.25516597
20140615 14364.49877 42.3 -149009 101.2599086 90.21162401 99.41102646 113.8223066 106.882019 95.41463845
20140622 14247.61966 96.83 -770514 100.4567147 89.08972771 98.97519954 112.9834058 106.223249 94.56735706
20140629 13867.25553 39.38 6887811 99.6472971 87.9634136 98.53930851 112.1414406 105.5644542 93.71707258
20140706 13952.17408 164.83 -325146 98.83072013 86.83582833 98.10340143 111.2986265 104.9057175 92.86592928
20140713 13606.93546 0.54 -918370 98.00603987 85.70942995 97.66749426 110.4568349 104.2471717 92.01561203
20140720 12365.85288 -44.03 -7988279 97.172303 84.58731944 97.23160275 109.6183516 103.5891114 91.16824759
20140727 13382.69317 -37.88 9406327 96.32854576 83.47492055 96.795774 108.786803 102.9322445 90.32754231
20140803 13215.86645 -135.61 -3038838 95.47379277 82.3829178 96.3601809 107.9686256 102.2782929 89.50076485
20140810 12929.63734 -170.46 -2523942 94.60705598 81.33304427 95.92546843 107.175674 101.6314575 88.70263453
20140817 12262.72744 112.9 281306 93.72733345 80.36985449 95.49404583 106.4300251 101.0019889 87.96319539
20140824 11969.15285 170.51 1977123 92.83360826 79.58485954 95.07491354 105.7729235 100.4149026 87.3438882
20140831 12274.05186 81.17 -3339063 91.92484727 79.16609864 94.70169502 105.2814952 99.93124606 86.97041804
20140907 11168.15524 -22.27 -6063453 91 79.5 94.5 105.1 99.7 87.1
20140914 10709.79633 -245.73 4015437 92.86247344 81.38070895 94.93907809 105.4982892 100.0847369 88.25895582
20140921 10817.08069 14.13 2381477 94.70994447 84.01080341 95.54967284 106.2058062 100.7218914 89.91898182
20140928 10663.75135 -202.08 555739 96.54454869 86.99880344 96.2061539 107.0772998 101.4624966 91.8198207
20141005 9763.12662 99.82 -7200394 98.36840687 90.14710652 96.87482219 108.0339656 102.2455358 93.83003752
20141012 10960.82462 -350.86 11107201 100.1836273 93.34494631 97.54639523 109.0315122 103.0460689 95.87670885
20141019 9997.050868 -150.34 -5939264 101.9923084 96.50839046 98.21741005 110.0422145 103.8540284 97.90641251
20141026 9944.235397 54.43 3005273 103.7965409 99.53487456 98.88328534 111.043678 104.6656602 99.85664947
20141102 9784.14586 337.87 -695123 105.5984105 102.2476429 99.52914291 112.0097644 105.4803329 101.6224102
20141109 9546.99488 -71.79 314380 107.4 104.3 100.1 112.9 106.3 103
20141116 9244.900155 65.78 17325 107.1137614 104.9875055 100.3906136 113.6431749 107.1308611 103.5780729
20141123 8808.63662 141.05 -2007912 106.8289945 105.0108012 100.6062109 114.3073978 107.966287 103.7653034
20141130 9175.051622 -106.01 6162136 106.5453701 104.7107263 100.8017273 114.928784 108.8035298 103.7612856
20141207 9101.637737 192.83 -3773128 106.2625603 104.2529501 100.9918666 115.526091 109.6413859 103.6633056
20141214 9172.5426 -243.29 558458 105.9802382 103.7177153 101.1805602 116.1083186 110.4791731 103.5183848
20141221 9123.711552 44.47 659235 105.6980775 103.143305 101.368843 116.6783565 111.3161508 103.3484336
20141228 7537.97184 184.01 -15376329 105.4157519 102.5467811 101.5569268 117.2341545 112.1508902 103.1619725
20150104 8631.193364 75.88 15715797 105.1329351 101.9332268 101.7446246 117.7678923 112.9800594 102.9586901
20150111 8838.39816 -94.29 -1215958 104.8493002 101.2984994 101.9309764 118.2628103 113.7955908 102.7292765
20150118 9033.024336 -14.26 1006714 104.5645194 100.6269873 102.1123252 118.6864184 114.5778228 102.4504429
20150125 8806.864325 290.8 -4007234 104.2782633 99.88315215 102.2749908 118.9773302 115.2787831 102.0724634
20150201 8292.73749 -105.73 -1926215 103.9902012 98.99227849 102.3678625 119.0203884 115.781405 101.4925741
20150208 8117.578392 96.29 -3199204 103.7 97.8 102.2 118.6 115.8 100.5
20150222 6591.595891 117.27 -5681716 100.4025975 93.8762347 99.65545289 115.5624827 112.9907375 96.41397393
20150301 7429.726695 82.72 9511806 98.74542109 91.61746494 98.18301893 113.5652513 111.1458061 93.96348573
20150308 8641.626314 -48.07 1688606 97.08004277 89.28790494 96.69190424 111.4371422 109.2197128 91.41524066
20150315 8644.147128 -33.59 2397045 95.40453741 86.92474241 95.19579544 109.2411834 107.2605892 88.82044782
20150322 8632.189365 176.43 -36298 93.71696819 84.54696258 93.6983737 107.0129085 105.2884851 86.20541569
20150329 8322.389316 -226.5 -7388523 92.01538432 82.1656325 92.20068972 104.7747561 103.3124148 83.58562578
20150405 7574.663565 86.59 291948 90.29781882 79.78989601 90.70326885 102.5451621 101.3380208 80.97401444
20150412 7734.082764 2.67 6631101 88.56228623 77.43192706 89.20716356 100.3460307 99.37237042 78.38789311
20150419 8239.993245 -80.81 5176902 86.80678032 75.11352422 87.71606202 98.21160991 97.42996479 75.85820908
20150426 7965.947754 399.35 -6679423 85.02927178 72.87791905 86.24367723 96.20236985 95.54506699 73.44610063
20150503 7611.97648 -87.67 2149209 83.22770586 70.8131946 84.84122033 94.42950642 93.80083155 71.27560286
20150510 7948.371142 -89.57 34649 81.4 69.1 83.7 93.1 92.4 69.6
20150517 8049.740643 -160.19 1810588 81.80332784 68.1094081 83.53470096 92.60048813 91.83726572 68.93731922
20150524 7569.963872 50.53 -365999 82.1788692 67.47043556 83.63063861 92.54465555 91.61795711 68.76965605
20150531 7514.023004 33.85 1606634 82.52705819 67.00257124 83.79650464 92.72597471 91.53937295 68.84391462
20150607 7687.393563 -363.91 -3053101 82.84829731 66.61799333 83.98108926 93.03401668 91.51845182 69.03641459
20150614 7915.602594 -19.56 -8869002 83.1429579 66.27399626 84.17068452 93.40970316 91.52115568 69.2867286
20150621 6886.880676 -56.9 6446118 83.41138059 65.94985708 84.36162107 93.82136675 91.53353178 69.56535677
20150628 7376.858034 111.2 -986561 83.65387566 65.63560988 84.55291678 94.2519187 91.54985038 69.85794586
20150705 7046.740272 -49.1 250462 83.87072342 65.32664725 84.7443091 94.6919242 91.5677336 70.15761095
20150712 6616.456938 -363.14 1492804 84.06217456 65.02122441 84.93572903 95.1357792 91.58613371 70.46125243
20150719 6883.896656 138.28 -1012797 84.22845038 64.71951407 85.12716286 95.57943954 91.60444244 70.76789785
20150726 6811.449276 -351.76 2591536 84.36974309 64.42370003 85.31862474 96.01878221 91.62197384 71.07818172
20150802 6726.622821 -96.71 -2467667 84.48621602 64.13916011 85.51018505 96.44793455 91.63738107 71.39468492
20150809 6190.303924 -225.9 837044 84.57800381 63.8773793 85.70211132 96.8568946 91.6475114 71.72331535
20150816 6614.64462 -102.73 -961392 84.64521257 63.66217123 85.8954043 97.2274781 91.64472583 72.07646834
20150823 6496.243056 -588.27 6072919 84.68791999 63.54257797 86.09380286 97.52594979 91.61040467 72.47965752
20150830 6772.360023 306.99 -6443063 84.70617543 63.61943611 86.3112747 97.68935333 91.49911757 72.98516327
20150906 6770.10926 -35.86 -2063670 84.7 64.1 86.6 97.6 91.2 73.7
20150913 6673.061307 341.81 -1147600 84.88444623 65.41021135 87.15491213 97.03777899 90.44249953 74.84316328
20150920 6459.860415 71.93 -1159338 85.04466766 67.12168 87.78104647 96.22516514 89.49764482 76.19351728
20150927 6303.013815 -307.35 -6652058 85.18084951 69.02337687 88.42612902 95.28316911 88.47718145 77.64076239
20151004 5344.697037 245.84 5738194 85.29314919 71.00731951 89.0758477 94.2803733 87.42857478 79.1264307
20151011 5971.639212 135.35 4857241 85.38169653 73.01007798 89.72517866 93.26072284 86.37536355 80.61260869
20151018 6417.000734 150.45 -1530612 85.44659389 74.97841489 90.36832135 92.26133301 85.33717184 82.06165551
20151025 6091.730304 78.02 -194664 85.48791627 76.84044669 90.98707721 91.3275291 84.34639288 83.41606707
20151101 5904.13722 -135.28 4424636 85.50571145 78.46670435 91.5143898 90.53121314 83.47572883 84.56771435
20151108 6116.07632 131.14 -3583511 85.5 79.6 91.7 90 82.9 85.3
20151115 5822.92998 -424.55 -1145703 84.88631491 79.71610825 90.60906288 89.96892954 83.04477192 85.17199008
20151122 5767.492714 202.47 -2050808 84.24840717 79.33732238 89.17641702 90.20521808 83.48477546 84.6236336
20151129 5731.552584 -140.76 3725045 83.5855394 78.71785155 87.65230242 90.58442113 84.04573872 83.87001608
20151206 5951.292566 92.64 -1193352 82.89694534 77.98152586 86.10370541 91.04000605 84.65622617 83.01641905
20151213 5652.626049 -373.8 -353441 82.18182898 77.18887517 84.54856292 91.53639163 85.2869063 82.11464026
20151220 5785.745716 169.38 -1598964 81.43936368 76.36991804 82.99169828 92.05452992 85.9256148 81.19070272
20151227 5513.3874 146.13 -8992969 80.66869117 75.54042289 81.43448455 92.5841876 86.56701287 80.25877565
20160103 5094.713425 -58.39 12164545 79.86892056 74.71044964 79.87759543 93.11979924 87.20804829 79.32875429
20160110 5527.809588 -459.99 -1523971 79.03912736 73.88979924 78.32235496 93.65821207 87.84536054 78.41155334
20160117 5461.9691 -177.13 156369 78.17835233 73.09332286 76.77339014 94.1974084 88.47242373 77.52494577
20160124 5318.710272 135.06 3784776 77.28560045 72.34896669 75.24790103 94.73568808 89.07399857 76.70307039
20160131 5400.46584 350.51 -14671978 76.35983973 71.71291047 73.81012021 95.27097946 89.61317631 76.01469435
20160214 4178.142814 -187.59 10544886 76.36728862 71.34595914 72.81394648 96.31692609 90.0149858 75.74557513
20160221 5427.485784 339.14 -20231 77.30050686 71.61515673 73.25555361 96.82731243 89.87761243 76.16488483
20160228 5371.5115 65.59 -1313205 78.20073348 71.99288989 73.78486918 97.33406371 89.67782732 76.71782793
20160306 5165.937 275.36 4877965 79.06900912 72.42324943 74.33766102 97.83861161 89.45251732 77.33579013
20160313 5740.33275 89.74 1191735 79.90633748 72.87883391 74.89673065 98.34149495 89.21686873 77.984939
20160320 5487.371846 79.68 -5660239 80.71368649 73.3459062 75.45745685 98.84257804 88.9772783 78.64812531
20160327 5348.992638 -112.44 -271843 81.49198943 73.81695214 76.01853731 99.3409957 88.73679154 79.31570916
20160403 5426.780965 -108.56 -7778413 82.24214598 74.28661007 76.57937944 99.83480265 88.49769195 79.9804031
20160410 4641.285 -86.2 8104328 82.96502332 74.74876648 77.13891314 100.3201853 88.26344232 80.63337371
20160417 5279.359605 162.84 -687519 83.66145706 75.19324427 77.69344695 100.7899162 88.04162877 81.25951622
20160424 5220.884313 -140.66 -2782584 84.33225226 75.60028983 78.22927238 101.2304259 87.85041018 81.8293742
20160501 5028.013578 -178.79 -1434215 84.97818435 75.9298725 78.69519947 101.6163034 87.73395637 82.28338197
20160508 4828.00368 -205.7 3444823 85.6 76.1 78.9 101.9 87.8 82.5
20160515 4990.55814 -121.44 -3002432 85.08991782 75.94230317 78.12929004 101.9925612 88.31142764 82.23069763
20160522 4925.14642 115.17 2950158 84.55584837 75.6251029 77.09745341 101.9830062 89.00535577 81.72395896
20160529 4966.103024 326.24 3314837 83.99717427 75.23031703 75.99571799 101.9189748 89.7740574 81.10125203
20160605 5009.6448 128.53 -11823405 83.41324971 74.79783531 74.8752725 101.8260319 90.57337579 80.42200794
20160612 4435.354066 29.09 6766040 82.8033997 74.34712766 73.74982087 101.7180268 91.38518363 79.71541332
20160619 4728.062988 -21.98 1352997 82.16691925 73.887791 72.62303741 101.6027051 92.20197179 78.99602373
20160626 4876.042706 -205.97 1867808 81.50307263 73.42474789 71.49592818 101.484848 93.02046527 78.27155006
20160703 4765.53658 322.03 -6029749 80.81109244 72.96091316 70.36884655 101.368209 93.83883888 77.5469835
20160710 4288.459904 -0.09 8840149 80.09017877 72.49877277 69.24220124 101.2570352 94.65516551 76.82726554
20160717 4874.674527 224.38 -286372 79.33949827 72.04173053 68.11727507 101.1577886 95.46577777 76.11993141
20160724 4955.11527 77.75 -1708660 78.55818317 71.59595192 66.99879511 101.0817639 96.26215009 75.43914106
20160731 4766.036 -21.62 -1875898 77.74533029 71.17370391 65.90440311 101.049666 97.02365051 74.81325397
20160807 4671.079296 114.33 1505199 76.9 70.8 64.9 101.1 97.7 74.3
20160814 4751.418455 29.29 -218000 77.14196954 70.52615005 64.23177555 101.3046738 98.16852675 74.01736186
20160821 4553.755556 -147.68 -1827657 77.3507642 70.30089661 63.65354016 101.5918086 98.55189175 73.8474285
20160828 4579.152453 112.85 1070084 77.52662534 70.09930697 63.09939338 101.9229408 98.90035395 73.73258004
20160904 4650.93738 -85.15 2827400 77.66975625 69.90926713 62.55169524 102.2774351 99.23449902 73.64466417
20160911 4853.76 -0.87 -12242308 77.78032239 69.72491958 62.00572535 102.6441232 99.56274057 73.56993609
20160918 3653.548384 -28.1 7674658 77.85845156 69.54349032 61.46022579 103.0167778 99.88847326 73.50170579
20160925 4451.08752 225.93 -8661369 77.90423409 69.36378622 60.91488093 103.3915895 100.2129588 73.43675925
20161002 3707.56604 -9.82 5415594 77.91772289 69.18554864 60.36968507 103.7656229 100.5363985 73.37363907
20161009 4481.242982 92.27 -1090716 77.89893356 69.00931336 59.82493105 104.1356272 100.8581046 73.31186484
20161016 4135.71384 -161.12 2768651 77.84784437 68.83670082 59.28179686 104.496719 101.1759157 73.25167621
20161023 4343.89755 183.27 -492913 77.76439628 68.6712939 58.7447058 104.8404034 101.4843585 73.19416102
20161030 4125.372995 -50.74 -1816776 77.64849282 68.52057873 58.23018832 105.1511315 101.7699954 73.14183724
20161106 4042.326155 -180.23 6966518 77.5 68.4 57.8 105.4 102 73.1
;
proc model data=paper;
exog search_volume volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
instruments _exog_;
parms b0 b1 b2 b3 b4 b5 b6 b7 b8;
AvgPrice_change = b0 + b1*search_volum + b2*volume_change + b3*mood_price + b4*mood_TWeconomic + b5*mood_government + b6*mood_possibility + b7*mood_global + b8*mood_investment;
fit AvgPrice_change / gmm kernel=(bart,11,0) vardef=n;
run;
quit;

The code can't run. But I'm confused by the bug in the log.txt.

By the way, I want to ask how to run a function in different period.

ex.

AvgPrice_change(t) = b0 + b1*search_volum(t-1) + b2*volume_change(t-2) + b3*mood_price(t)

 

Thanks a million.

1 ACCEPTED SOLUTION

Accepted Solutions
Kurt_Bremser
Super User

I think I've found something:

proc model data=paper;
 exog search_volume volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
 instruments _exog_;
 parms b0 b1 b2 b3 b4 b5 b6 b7 b8;
 AvgPrice_change = b0 + b1*search_volume + b2*volume_change + b3*mood_price + b4*mood_TWeconomic + b5*mood_government + b6*mood_possibility + b7*mood_global + b8*mood_investment;
 fit AvgPrice_change / gmm kernel=(bart,11,0) vardef=n;
 run;
 quit; 

In your original code, you spelled search_volume as search_volum (note the missing"e") in the equation. I guess that is the reason.

View solution in original post

9 REPLIES 9
karen8169
Obsidian | Level 7
Data paper;
input date search_volume AvgPrice_change volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
datalines;
20131229 31265.96873 -8049968 101.6245784 85.46101172 100.6374401 115.9752537 118.0235693 93.97899654
20140105 27950.70473 -32.37 16673309 101.4670963 85.20483674 99.47578826 116.2290342 117.99756 93.82381802
20140112 25902.91541 34.49 -1744281 101.3045585 84.9599483 98.31558748 116.4796395 117.9683595 93.67532876
20140119 23850.62263 8.1 -2784694 101.1367771 84.74080987 97.1608941 116.7261614 117.9323331 93.54195118
20140126 16471.45342 -76.11 -17009604 100.9635581 84.57583371 96.02679831 116.9660368 117.8800425 93.44019161
20140202 18413.8849 -229.31 9574282 100.7847013 84.52277179 94.96965667 117.1940095 117.7882222 93.40347039
20140209 22096.8015 126.25 7354967 100.6 84.7 94.2 117.4 117.6 93.5
20140216 21174.52948 121.41 -35509 101.0706661 85.3506424 94.50432279 117.5650212 117.1765452 93.86927514
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;
proc model data=paper;
 exog search_volume volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
 instruments _exog_;
 parms b0 b1 b2 b3 b4 b5 b6 b7 b8;
 AvgPrice_change = b0 + b1*search_volum + b2*volume_change + b3*mood_price + b4*mood_TWeconomic + b5*mood_government + b6*mood_possibility + b7*mood_global + b8*mood_investment;
 fit AvgPrice_change / gmm kernel=(bart,11,0) vardef=n;
 run;
 quit; 

 

The code can't run. But I'm confused by the bug in the log.txt.

By the way, I want to ask how to run a function in different period.

ex.

AvgPrice_change(t) = b0 + b1*search_volum(t-1) + b2*volume_change(t-2) + b3*mood_price(t)

 

Thanks a million.

Kurt_Bremser
Super User

Disclaimer: I have absolutely no idea what proc model does, I'm just a data architect/engineer.

 

But from the log I guess SAS is complaining that you have 9 parameters in the parameters statement, but only 8 defined instruments via _exog and the exogenous statement. Somehow these must fit when only 1 equation is used.

karen8169
Obsidian | Level 7

Yes, I know what you say. But I  set parms from b0 to b8. I don't know why search_volume appears when I get the result.

parms b0 b1 b2 b3 b4 b5 b6 b7 b8

 

Kurt_Bremser
Super User

I think I've found something:

proc model data=paper;
 exog search_volume volume_change mood_price mood_TWeconomic mood_government mood_possibility mood_global mood_investment;
 instruments _exog_;
 parms b0 b1 b2 b3 b4 b5 b6 b7 b8;
 AvgPrice_change = b0 + b1*search_volume + b2*volume_change + b3*mood_price + b4*mood_TWeconomic + b5*mood_government + b6*mood_possibility + b7*mood_global + b8*mood_investment;
 fit AvgPrice_change / gmm kernel=(bart,11,0) vardef=n;
 run;
 quit; 

In your original code, you spelled search_volume as search_volum (note the missing"e") in the equation. I guess that is the reason.

karen8169
Obsidian | Level 7

Thanks, I solve the first question. And I want to ask how to run the regression in different periods.

karen8169
Obsidian | Level 7

Well, I have used "by statement" and I think that it is used to categorize the data. But I want to run each variable, which starts in the different week so that the amount of the data will decrease. The form of model is like arma. Thanks.

ex.

    AvgPrice_change(t) = b0 + b1*search_volum(t-1) + b2*volume_change(t-2) + b3*mood_price(t)

BCO
Calcite | Level 5 BCO
Calcite | Level 5

Sort your dataset by time.  Create new variables that equal the lagged value of the variables you are regressing on.  Then run the model with the newly created lagged variables.

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