bootstrap <var >标签有什么用

bootstrap <var >标签有什么用

全局样式 1 Bootstrap 中用到一些 HTML元素和CSS属性需要将页面设置为 HTML5 文档类型，即在页面顶部添加“

"garchm" <- function(rtn,type=1){ # estimation of a gaussian garch(1,1)-m model. ##### the program uses garch(1,1) results as initial values. # rtn: return series # type = 1 for variance-in-mean # = 2 for volatility-in-mean # = 3 for log(variance)-in-mean # if(is.matrix(rtn))rtn=c(rtn[,1]) garchmdata <<- rtn # obtain initial estimates m1=garch11fit(garchmdata) est=as.numeric(m1separ);ht=m1coefficients[1]) gam=as.numeric(m2coefficients[2]) params=c(mu=cnst,gamma=gam, omega=cc, alpha=ar,beta=ma) lowbounds=c(mu=mean-4*semean,gamma=-10*abs(gam), omega=cc-2*secc, alpha=s, beta=ma-2*sema) uppbounds=c(mu=mean+4*semean,gamma=10*abs(gam), omega=cc+2*secc ,alpha=ar+2*sear,beta=1-s) vtmp <<- c(type,v1[1]) # fit=nlminb(start = params, objective= glkm, lower=lowbounds, upper=uppbounds, control=list(trace=3,rel.tol=1e-5)) epsilon = 0.0001 * fitpar npar=length(params) hessian = matrix(0, ncol = npar, nrow = npar) for (i in 1:npar) { for (j in 1:npar) { x1 = x2 = x3 = x4 = fitpar x1[i] = x1[i] + epsilon[i]; x1[j] = x1[j] + epsilon[j] x2[i] = x2[i] + epsilon[i]; x2[j] = x2[j] - epsilon[j] x3[i] = x3[i] - epsilon[i]; x3[j] = x3[j] + epsilon[j] x4[i] = x4[i] - epsilon[i]; x4[j] = x4[j] - epsilon[j] hessian[i, j] = (glkm(x1)-glkm(x2)-glkm(x3)+glkm(x4))/ (4*epsilon[i]*epsilon[j]) } } cat("maximized log-likehood: ",glkm(fitpar),"\n") # step 6: create and print summary report: se.coef = sqrt(diag(solve(hessian))) tval = fitpar, se.coef, tval, 2*(1-pnorm(abs(tval)))) dimnames(matcoef) = list(names(tval), c(" estimate", " std. error", " t value", "pr(>|t|)")) cat("\ncoefficient(s):\n") printcoefmat(matcoef, digits = 6, signif.stars = true) m3=resivol(fitresiduals,sigma.t=m3sigma.t) } glkm = function(pars){ rtn <- garchmdata mu=pars[1]; gamma=pars[2]; omega=pars[3]; alpha=pars[4]; beta=pars[5] type=vtmp[1] t=length(rtn) # use conditional variance if(type==1){ ht=vtmp[2] et=rtn[1]-mu-gamma*ht at=c(et) for (i in 2:t){ sig2t=omega+alpha*at[i-1]^2+beta*ht[i-1] ept = rtn[i]-mu-gamma**sig2t at=c(at,ept) ht=c(ht,sig2t) } } # use volatility if(type==2){ ht=vtmp[2]^2 et=rtn[1]-mu-gamma*vtmp[2] at=c(et) for (i in 2:t){ sig2t=omega+alpha*at[i-1]^2+beta*ht[i-1] ept=rtn[i]-mu-gamma*sqrt(sig2t) at=c(at,ept) ht=c(ht,sig2t) } } # use log(variance) if(type==3){ ht=exp(vtmp[2]) et=rtn[1]-mu-gamma*vtmp[2] at=c(et) for (i in 2:t){ sig2t=omega+alpha*at[i-1]+beta*ht[i-1] ept=rtn[i]-mu-gamma*log(abs(sig2t)) at=c(at,ept) ht=c(ht,sig2t) } } # hh=sqrt(abs(ht)) glk=-sum(log(dnorm(x=at/hh)/hh)) glk }

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