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1、%ADF检验法p取2.%AR(2)过程临界值的确定T=5000;N=200;w1=zeros(1,T);w2=zeros(1,T);for t=1:T sita1=0.7; sita2=0.3; ru=sita1+sita2; labuda1=-sita2; %p=2; e=randn(1,N); y1(1)=e(1); y1(2)=sita1*y1(1)+e(2); for j=3:N y1(j)=sita1*y1(j-1)+sita2*y1(j-2)+e(j); end dertay1(1)=y1(1); for i=2:N dertay1(i)=y1(i)-y1(i-1); end %数
2、据的生成过程 y(1)=e(1); for k=2:N y(k)=ru*y(k-1)+labuda1*dertay1(k-1)+e(k); end dertay(1)=y(1); for h=2:N dertay(h)=y(h)-y(h-1); end A=sum(dertay(1,1:N-1).2) sum(dertay(1,1:N-1).*y(1,1:N-1); sum(dertay(1,1:N-1).*y(1,1:N-1) sum(y(1,1:N-1).2); B=sum(dertay(1,1:N-1).*y(1,2:N) sum(y(1,1:N-1).*y(1,2:N); C=inv(
3、A)*B; ruhat=C(2,1); labuda1hat=C(1,1); w1(t)=N*(ruhat-1)/(1-labuda1hat); %样本方差计算SigmaSquare episilon1(1)=y(1)-arfahat; for i1=2:N episilon1(i1)=y(i1)-ruhat*y(i1-1)-labuda1hat*dertay1(i1-1);%Situation1与Situation2再此处不一样。 end SigmaSquare=(N-3)(-1)*sum(episilon1(1,1:N).2);%有三个参数需要估计，所以是N-3。 YitahatSquar
4、e=SigmaSquare.*0 1*inv(A)*0 1;%在计算YitaSquare时采用了小书p66结论。 %SigmaSquare/sum(y(1,1:N-1).2); %Statistic2 w2(t)=(ruhat-1)/(YitahatSquare(1/2);enda1=sort(w1);a2=sort(w2);b1=a1(fix(T*0.05)b2=a2(fix(T*0.05)%在备择假设下的检验for t=1:T sita1=0.3; sita2=0.3; ru=sita1+sita2; labuda1=-sita2; %p=2; e=randn(1,N); y1(1)=e(
5、1); y1(2)=sita1*y1(1)+e(2); for j=3:N y1(j)=sita1*y1(j-1)+sita2*y1(j-2)+e(j); end dertay1(1)=y1(1); for i=2:N dertay1(i)=y1(i)-y1(i-1); end %数据的生成过程 y(1)=e(1); for k=2:N y(k)=ru*y(k-1)+labuda1*dertay1(k-1)+e(k); end dertay(1)=y(1); for h=2:N dertay(h)=y(h)-y(h-1); end A=sum(dertay(1,1:N-1).2) sum(de
6、rtay(1,1:N-1) sum(dertay(1,1:N-1).*y(1,1:N-1); sum(dertay(1,1:N-1) N sum(y(1,1:N-1); sum(dertay(1,1:N-1).*y(1,1:N-1) sum(y(1,1:N-1) sum(y(1,1:N-1).2); B=sum(dertay(1,1:N-1).*y(1,2:N) sum(y(1,1:N) sum(y(1,1:N-1).*y(1,2:N); C=inv(A)*B; ruhat=C(3,1); labuda1hat=C(1,1); arfahat=C(2,1); w1(t)=N*(ruhat-1)
7、/(1-labuda1hat); %样本方差计算SigmaSquare episilon1(1)=y(1)-arfahat; for i1=2:N episilon1(i1)=y(i1)-ruhat*y(i1-1)-labuda1hat*dertay1(i1-1);%Situation1与Situation2再此处不一样。 end SigmaSquare=(N-3)(-1)*sum(episilon1(1,1:N).2);%有三个参数需要估计，所以是N-3。 YitahatSquare=SigmaSquare.*0 0 1*inv(A)*0 0 1;%在计算YitaSquare时采用了小书p66结论。 %SigmaSquare/sum(y(1,1:N-1).2); %Statistic2 w2(t)=(ruhat-1)/(YitahatSquare(1/2);ends1=w1b1;%等于“1”表示ruhat小于1拒绝单位根过程s2=w2b2;%等于“1”表示ruhat小于1拒绝单位根过程v1=sum(s1);v2=sum(s2); 3
