[软体程式类别]: RATs [程式问题]: ANST-GARCH [软体熟悉度]: 中(3个月到1年) [问题叙述]: 想请问有没有人写过双变量ANST-GARCH的程式码 因为最近在写这个程式码的时候遇到了一个奇怪的问题 就是跑一次程式码跟跑第二次会跑出不同的结果 但是如果有关闭RATs程式之後，再重跑一次结果又会跟原本第一次的结果相同 我是以之前写的双变量GARCH-M模型当作基础， 再加入smooth transition function的部分 [程式范例]: compute n=2 compute gstart=1,gend=1555 * dec series[vect] Y dec frml[vect] R dec vect[series] U(n) dec series[vect] ECT * * The paths of the covariance matrices and uu' are saved in the * SERIES[SYMM] named H and UU. UX and HX are used for the current values * of the residual vector and H matrices * declare series[symm] G uu * * ux is used when extracting a u vector * declare symm Gx(n,n) declare vect ux(n) * * These will be the parameters for the mean equations. These are adjusted to add * variance or covariance terms as needed. * dec vect EC(n) C1(n) C2(n) C3(n) dec rect T1(n,n) T2(n,n) T3(n,n) F1(n,n) F2(n,n) F3(n,n) dec frml[vect] Ts dec frml[vect] Tf dec frml[symm] Vs dec frml[symm] Vf * *construct a error correct term * linreg LSP / ECTEQ # constant LF1M gset ECT = ||ECTEQ,ECTEQ|| nonlin(parmset=standmean) EC C1 T1 F1 C2 C3 T2 T3 F2 F3 nonlin(parmset=transmean) L * * Mean model = VAR(1) with sqrt(G) "M" term * frml R = Y-EC.*ECT{1}-C1-C2.*Ts-C3.*Tf-T1*Y{1}-T2*Y{1}.*Ts-T3*Y{1}.*Tf-F1*%sqrt(%xdiag(G))-F2*%sqrt(%xdiag(G)).*Ts-F3*%sqrt(%xdiag(G)).*Tf gset Y = ||dLSP|dLF1M|| * * Run preliminary VAR(1) to get estimates of residuals * * linreg dLSP / u(1) # constant dLSP{1} dLF1M{1} ecteq{1} linreg dLF1M / u(2) # constant dLSP{1} dLF1M{1} ecteq{1} * linreg dLSP # constant dLSP{1} DLF1M{1} u(1) u(2) ecteq{1} compute C1(1)=%beta(1),$ T1(1,1)=%beta(2),$ T1(1,2)=%beta(3),$ F1(1,1)=%beta(4),$ F1(1,2)=%beta(5),$ EC(1)=%beta(6) linreg dLF1M # constant dLSP{1} dLF1M{1} u(1) u(2) ecteq{1} compute C1(2)=%beta(1),$ T1(2,1)=%beta(2),$ T1(2,2)=%beta(3),$ F1(2,1)=%beta(4),$ F1(2,2)=%beta(5),$ EC(2)=%beta(6) compute L = 14 vcv(matrix=WWW) # u * * These are used to initialize pre-sample variances. * gset G * gend = WWW gset uu * gend = WWW set u(1) = 0.0 set u(2) = 0.0 * declare frml[symm] Gf * frml logl = $ Gx = Gf(t) , $ G(t) = Gx, $ ux = R , $ uu(t) = %outerxx(ux), $ %pt(u,t,ux),$ %logdensity(Gx,ux) * * *create smooth translation function * frml rs = u(1) frml rf = u(2) frml fs = %logistic(L*rs{1},1) frml ff = %logistic(L*rf{1},1) frml Ts = ||fs|fs|| frml Tf = ||ff|ff|| frml Vs = ||fs|fs,fs|| frml Vf = ||ff|ff,ff|| * * This does a BEKK model for the variance * dec symm A1(n,n) B1(n,n) D1(n,n) dec rect AA2(n,n) AA3(n,n) BB2(n,n) BB3(n,n) CC2(n,n) CC3(n,n) compute AA2=.05*%identity(n),AA3=.50*%identity(n) compute A1=%decomp(WWW) nonlin(parmset=standgarch) A1 AA2 AA3 B1 BB2 BB3 D1 CC2 CC3 frml Gf = A1*tr(A1)+AA3*G{1}*tr(AA3)+AA2*uu{1}*tr(AA2)+B1*tr(B1).*Vs+BB3*G{1}*tr(BB3).*Vs+BB2*uu{1}*tr(BB2).*Vs+D1*tr(D1).*Vf+CC3*G{1}*tr(CC3).*Vf+CC2*uu{1}*tr(CC2).*Vf maximize(trace,parmset=standmean+standgarch,pmethod=simplex,piters=200,method=bfgs,iters=150) logl gstart+2 gend maximize(trace,parmset=standmean+transmean+standgarch,pmethod=simplex,piters=200,method=bfgs,iters=150) logl gstart+2 gend 以上是很恐怖的程式码 >< ----------------------------------------------------------------------------- --

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