--VAR模型的构建

1、VAR模型的构建例：上证指数与投资者的数据分析Vie行| Prod 此词5|时|5E后|如团部卜| 51To什|七哥| 58词 的除岁|8国|5期北|Range. 1 963 - 963 ot)sSample: 1 963 - 963 ot)sDisplay Filter *必 dOund 回 dMnO1 0 diLinO2 M dif_intiarik ,区 HifJnHeix fund啊 H tund_&eilhptrendOI 0 inB Juy ,0 in01 _sel M tnO2_buy 0 in02_sell g inbank_tou 回 inbanOell I 必 in

2、aevsh 必 ln02_buySS000SSSInmdex 但sid residOI resid02 resid02 reslcfO4 resid05 residOS residOZ residOS应用 Eviews 软件,创立 VAR 对应选择 Quick/Estimate VAR,或选择 Objects/new object/VAR , 也可以在命令窗口直接键入VAR选择VAR滞后选择的选取：根据信息法那么不断调试最优阶数Engogenour variable :内生变量的设置,根据 VAR模型的设计,所有变量都可以作为内生变 量,所以不依据顺序随意输入数据.在彳VAR模型时,不同类型的

3、时间序列必须进行平稳检验,最好选择平稳的时间序列来做说明：此时的模板数据已经做了一阶差分,即指数的一阶差分序列一上证指数.本例子中选取了三类投资者：基金、散户、券商,同时给出了彼此的买价和卖价Vedo r Auto regression EstimatesDate: 03/30/11 Time:15:18Sample (adjusted 5 963Included obser/iitiors: 95g after adjustmentsStandard errors in () & t-staiishcs in |DIFJN02DIF.FUMDDIFJNBAKJKIFJNDEXDIFJ

4、NOif-1).0.3410350 0345290 D20345-D.633707CG.J3371)(D.054O1)(0 01756(1.78145)1 0.11 56(0.641 U1,159361-0.35015|DIFJNU2(-2)0.107431Q.0M3470.035G95'2.336153C0,IJ3551)(0.06690)(0.01549)(h87&59)3,0250211.306731,941 02M.24&12DIFJNO2(-3)0.110651-0124142-0.01 25330.696859(0.03536)(D.05B65)<0,

5、01841)(1.06359)3,12900卜2 1gl 25-070266DI FjNiJZgi)0.0990Q1-0.0287270.D00S56"1542464C0,l1330e)(0.05300)(001 T2T(1.74798)(2,99274I-0.54205-0,055531J5452IOQFUNDE)'.0214010 30660OJ0470e0.775221(O.t)2247>(0.03600)(0,0117C)(1416745)-0,55233tS.52069*0,402460.652C5CilF_FUND<-2)-0.0246010 0467

6、490 0046601.0101 38rn c、r i -rirni nr-tLfrri nd rd nir $r c m第一个是估计值,第二个误差,第三个是标准差.主要是为了判断变量的显著性.| 4V Q JL 5FF3WJ| V . y r w DJL W. YRsquared0.31 3&97D.133S920.050060.01 2200Adj. R-siiuared0.30712511191010.C33932-0.004579Sum sq. resids0.6109721.5631D20.15552517O5.SO2S.E. equahon0.0254671D40BOO0.

7、C1225G1.345710F-stetistic27.540359.1014912.1030400.727152Leg likeEihood2167.&8417167222793.3774636.934AKaikeAIC-4,JS52643 5426947911933.44999Schwarz SC4.399007-3456438-5.7049363 53554 6Moan dependent-0.0085910.010434-0.000603-0.030888S.D. dependent0 0305960.0434720.0124S71.342641Determinant resi

8、d covanance (dofadj.)2 55E-10Determinant resid covariance247E0Log likelihood5164.452Akaike hformatiori criterlor,M 一 62.鸵Schwarz criteri on-10.25365判断滞后阶数的信息准那么需要看下面的信息准那么,对应的数字越小,就是应选择最优阶数.之后做脉冲效应, view下选择impulse response,即建立脉冲反响.Display 工叩".X 口号finite94Bj. splay form at- Tiblfl Mnil 1 i pl &a

9、mp; (jtapltEI .". Combi it ad GraphsBesponse Staitdar d ErrorsDixplsy Informati onImpuls 目五：;dif_inQ2 dif.fuad di£_inbMik <kEmd物ReEpOnEfiE.dif-inDE aif.fund di F_j nb aiL di £_i md毕*P er i ods 10匚R号名P9I1二襦定取消Display format :表、两个关系的图、多重图Impulse :冲击responses:四个变量都响应Period ：滞后阶数确定后得出的

10、图为Response of DIF_IN02 to DIF_IN02Response of DIF_FUND to DIF_IN02.06.04 _.02 _.00 a .二-二 - .02 _12345678910Response of DIF_INBANK to DIF_IN02Response to CholeskyResponse of DIF_IN02 to DIF_FUND.03.02.01 -.00- .01 _12345678910Response of DIF_FUND to DIF_FUNDOne S.D. Innovations ±2 S.E.Response

11、of DIF_IN02 to DIF_INBANKResponse of DIF_FUND to DIF_INBANKResponse of DIF_IN02 to DIF_INDEXResponse of DIF_FUND to DIF_INDEXResponse of DIF_INBANK to DIF_FUNDResponse of DIF_INBANK to DIF_INBANKResponse of DIF_INBANK to DIF_INDEXResponse of DIF_INDEX to DIF_IN02Response of DIF_INDEX to DIF_FUNDResp

12、onse of DIF_INDEX to DIF_INBANK1.51.0 -0.5 -0.0- 0.5 -+11R1rf112345678910Response of DIF_INDEX to DIF_INDEX第一组表示自己对于影响,第二组表示散户投资者对于指数的影响,第三组表示券商都是指1'* Analytic (asymptoi M*ntft Csrlc数的影响-第一排指数是受影响的对象第二排中散户是受影响的对象.-评价：该图反响效果不好,一般选用合成图Response of DIF_IN02 to CholeskyOne S.D. InnovationsResponse of

13、 DIF_FUND to CholeskyOne S.D. InnovationsDIF_FUNDDIFINDEXDIF_IN02 DIFINBANKDIF_FUNDDIFINDEXDIF_IN02 DIFINBANKResponse of DIF_INBANK to CholeskyOne S.D. InnovationsResponse of DIF_INDEX to CholeskyOne S.D. InnovationsDIF_FUNDDIFINDEXDIF_IN02 DIFINBANKDIF_FUNDDIFINDEXDIF_IN02 DIFINBANK第四个图表示指数变量对其它三个变

14、量的影响,回归平均线表示影响的滞后期.如果线在水平线上方,可以看出影响方向为正方,假设在水平线下方,那么影响方向为负方.脉冲响应告诉我们：影响的时间、影响的方向.在估计结果窗口,实现方差分解的操作一直接建立在脉冲响应的根底上.在view的菜单栏里面,选择 variance decomposition o结果为Vaiiance DecompositionVariance D9composition crfDIFJNOS:PeriodS.E.DIF_IN02DIFJUNDDIFJNBANKDIFJNDEX10 025467100 0000O.OOODOOOOOODOO000000020.D2752

15、307.495990 2424694.02E-O5226047330 O282Z796.647860.27S5480917467215612940.02901296.343220.3756251 2237552.05739S50.02974996,308240.3573691.22C4082.10636160 03009296.2510&0 3592541.267911212177570 03032696.1 91BB0.3606891 3577392.09966B80.03050096.1 4GS10.3e33101.4147502.07303300 D30G1G96.134740

16、3625291.43295920G9772100 03069606.1221 i0 3622521.4515682.OB4066Variance Decomposition ufDIF_FUND:PeriodEEDIFJN02DiF_FUNDDfFJNSANKDIFJNDEX10 040000670S1O393.293890.000000OOOODCO20.0424466.43830893.368920.1B9U10.0032433Q.0420306.4210739307S150.1950410.30573340,0433126.4204719122303Q.294S912.05164950 0436266.JS08120O.2C95D07947952534&S260.0437076.4B445969.990130.016