eviews中的蒙特卡洛模拟程序.doc

模拟程序案例例 1，在做抛掷一枚质地均匀的硬币的试验中发“正面朝上”的事件（用 1 表示）和“正面朝下”的事件 A（用 0 表示）的情况。历史上一些学者得到的具体试验结果如下：现在需要利用 eviews 来模拟上述三位学者的实验。算法分析：上述三学者的实验均为二项分布的实验，可以直接利用 eviews 产生二项分布随机数的函数rbinom（n，p）.编程如下：workfile binom u 1 2048series resultfor !i=0 to 1smpl 1 2048series xx(1)=0for !cou=1 to 2048x(!cou)=rbinom(!i,0.5)nextnextx.hist例 2（投掷骰子）（1）投掷一颗质地均匀的骰子，令 X 表示其出现的点数，分析各点数出现的频率的稳定性及变化规律；（2）利用统计的方法，根据“频率的稳定性”规律求投掷一枚质地不均匀的骰子出现某点数的概率；（3）演示随机变量 X 的数学期望的统计意义。算法分析：根据逆变换法产生来自分布函数F（x）的随机数，就要求出 F-1（y），其中 F-1（y）infx：F（x）y.0y1.质地均匀的骰子各点数出现的频率的分布函数是F（x）=p（xx）=（i=1）/6，i-1xi，i=1，2，7可求得F-1（y）=infx:F（x）y.0y1=i-1，（i-1）/6yi/6，i=1，6因而，可先由产生均匀分布随机数的函数 runif（0，1）抽取 y 值，再来计算 F-1（y）值即可。程序实现：workfile binom u 1 1000smpl 1 1000series x series yseries a1series a2series a3series a4series a5series a6for !i=1 to 1000a1(!i)=1/6a2(!i)=2/6a3(!i)=3/6a4(!i)=4/6a5(!i)=5/6a6(!i)=1x(!i)=runif(0,1)if x(!i)=a1(!i) and x(!i)=a2(!i) and x(!i)=a3(!i) and x(!i)=a4(!i) and x(!i)=a5(!i) and x(!i)a6(!i) then y(!i)=6else y(!i)=7endifendifendifendifendifendifnexty.hist1.通过已知总体模型得到多组样本数据，进行多次回归，验证回归结果的特征、性质最小二乘法的无偏性workfile mc u 1 10vector(10) v1v1.fill 80, 100,120,140,160,180,200,220,240,260mtos(v1,x)!b1=25!b2=0.5matrix(100,2) ffor !k=1 to 100series u=3*nrndseries y=!b1+!b2*x+uequation eq.ls y=c(1)+c(2)* xf(!k,1)=c(1)f(!k,2)=c(2)nextshow fexpand 1 100smpl 1 100mtos(f,gr)freeze ser01.qqplotfreeze ser01.histfreeze ser02.qqplotfreeze ser02.histmatrix(1,2) mm(1,1)=mean(ser01)m(1,2)=mean(ser02)show m蒙特卡洛模拟程序：(最终调试成功)store monte carle results in a serieschecked 4/1/2004set workfile range to number of monte carle replicationswfcreate mcarle u 1 100create data series for xnote: x is fixed in repeated samplesonly first 10 observations are used (remaining 90 obs missing)series x x.fill 80,100,120,140,160,180,200,220,240,260set true parameter values!beta1=2.5!beta2=0.5set seed for random number generatorrndseed 123456assign number of replications to a control variable!reps=100begin loopfor !I=1 to !reps set sample to estimation sample smpl 1 10 simulate y data (only for 10 obs) series y=!beta1+!beta2*x+3*nrnd regress y on a constant and x Equation eq1.ls y c xset sample to one observationsmpl !I !iand store each coefficient estimate in a series series b1=eq1.coefs(1)series b2=eq1.coefs(2)nextend of loopset sample to full samplesmpl 1 100show kernel density eatimate for each coeffreeze(gra1) b1.distplot kerneldrow vertical dashline at true parameter valuegra1.draw(dashline,bottom,rgb(156,156,156) !beta1show gra1freeze(gra2) b2.distplot kerneldraw vertical dashline at true parameter valuegra2.draw(dashline,bottom,rgb(156,156,156) !beta2show gra2一元回归参数的分布：Subroutine moni (scalar n, scalar sum, scalar param1, scalar param2, scalar type)For !m=1 to nX(!m)=rnd*100NextFor !n=1 to sumIf type=0 thenFor !m=1 to nU(!m)=nrndNextEndifIf type=1 thenFor !m=1 to nIf rnd0.5 thenU(!m)=rndElseU(!m)=rnd*(-1)EndifNextEndifIf type=2 thenU(1)=nrndFor !m=2 to nU(!m)=u(!m-1)+nrndNextEndifIf type=3 thenFor !m=1 to nIf rnd=2freeze(statby_%1) %1.statby(nomean,nostd)cao_%1nextfor %1 r0 b0 t0 dw0 r20 f0 r1 b1 t1 dw1 r21 f1 r2 b2 t2 dw2 r22 f2freeze(hist_%1) %1.histnext伪回归相关系数模拟（不行）workfile corr u 1 500series resultfor !i=1 to 500smpl 1 100series x=nrndseries y=nrndseries xxseries yyscalar sum1=0scalar sum2=0for !counter=1 to 100sum1=sum1+x(!counter)sum2=sum2+y(!counter)xx(!counter)=sum1yy(!counter)=sum2nextnextscalar r=cor(xx,yy)result(!i)=r时变贝塔系数的模拟（执行不了）： BETA.PRG (3/7/2007) Time varying beta demonstrates several ways to obtain beta between assets 1) constant beta 2) rolling beta by regression/moving cov/var 3) using state space 4) using multivariate ARCH Checked 3/20/2007change path to program path%path = runpathcd %path load workfileload fx.wf1 dependent variables of series must be continuoussmpl allseries y1 = pch(index)series y2 = pch(jy)- calculate the constant beta using OLS-smpl 1990 lastequation constant_beta.ls y2 c y1series beta_const=c(2)- calculating time varying beta with rolling regression for a bi-variate case can use moving cov/var instead of OLS regression-!ssize = 200series beta_roll=movcov(y1,y2,!ssize)/movvar(y1,!ssize) code for running a rolling regression: commented out right now!length = obs(y1)equation roll_beta.ls y2 c y1show roll_betafor !i = 1 to !length-!ssize+1 smpl first+!i-1 first+!i+!ssize-2 equation roll_beta.ls y2 c y1 smpl first+!i+!ssize-2 first+!i+!ssize-2 beta_roll = roll_beta.coefs(2)next- calculate beta with State Space via a time-varying coefficient for Y1-smpl 1990 lastsspace ssbetassbeta.append y2=c(1)+sv1*y1+var=exp(c(2)ssbeta.append state sv1 = sv1(-1)ssbeta.mlssbeta.makestates beta_*rename beta_sv1 beta_ss- calculate beta with system ARCH by estimating the covariance and variance of the two series using Multivariate ARCH-system arbetaarbeta.append y1 = c(1) arbeta.append y2 = c(2)arbeta.arch Diagvech c(indef) arch(1,indef) garch(1,indef)arbeta.makegarch(name=arch)series beta_arch = arch01_02/arch01- display the different betas-group betas_ls_roll beta_const beta_rollgroup betas_roll_ss_arch beta_roll beta_ss beta_archshow betas_ls_roll.lineshow betas_roll_ss_arch.line在生成服从某种分布（标准正态分布白噪声序列）的随机序列的基础上 生成各种ARIMA序列：AR(1)、MA(1)、ARMA(1,1)、ARIMA(1,1,1)n Workfile random2 u 1 1000n Series u=nrnd n U(1)=0n Series x1n X1(1)=0n Series x2n X2(1)=0n Series x3n X3(1)=0n Series x4 n X4(1)=0n Smpl 2 1000n x1=0.8*x1(-1)+un X2=u+0.8*u(-1)n X3=0.8*x3(-1)+u+0.8*u(-1)n X4=x4(-1)+x3n Smpl al