关于开源金融计算学习的总结（截至2017年1月3日）

#import data as mydata
#Becareful, the data file format may be various
mydata<-read.table(file="release_new_1.csv",head=TRUE,sep=",");
#add the gap of iC and iA
mydata$iGap<-mydata$iA-mydata$iC;

#season adjustment, and smooth
#"real", "bot"
mydata$real_sm<-filter(mydata$real,rep(1/12,12));
mydata$bot_sm<-filter(mydata$bot,rep(1/12,12));
mydata<-mydata[6:81,1:19];


#Do regression with 2SLS manually
#e1_adj<-real_sm~1+M2_fit+loan_fit+loan_1+iC;
#e2_adj<-ind~1+M2_fit+loan_fit+loan_1+iC;
#e3_adj<-f~1+real_fit+ind_fit+sp+iA+ex+bot;
#e4_adj<-M2~1+f_fit+loan_fit+loan_1;
#e5_adj<-loan~1+iC+req+cb_dep;
#adjusted equations
e1_adj<-real_sm~1+M2_fit+f_fit;
e2_adj<-ind~1+M2_fit+loan_fit+loan_1;
e3_adj<-f~1+real_fit+ind_fit+ex+bot_sm;
e4_adj<-M2~1+f_fit+loan_fit;
e5_adj<-loan~1+iC+req+cb_dep+real_fit;

#Get the fitted endogenous values;
mydata$real_fit<-lm(
real_sm~iC+ex+bot_sm+req+cb_dep+loan_1,
data=mydata)$fitted.values;
mydata$ind_fit<-lm(
ind~iC+ex+bot_sm+req+cb_dep+loan_1,
data=mydata)$fitted.values;
mydata$f_fit<-lm(
f~iC+ex+bot_sm+req+cb_dep+loan_1,
data=mydata)$fitted.values;
mydata$M2_fit<-lm(
M2~iC+ex+bot_sm+req+cb_dep+loan_1,
data=mydata)$fitted.values;
mydata$loan_fit<-lm(
loan~iC+ex+bot_sm+req+cb_dep+loan_1,
data=mydata)$fitted.values;

#fit the equations with the fitted endogenous values
res_e1_adj<-lm(e1_adj,data=mydata);
res_e2_adj<-lm(e2_adj,data=mydata);
res_e3_adj<-lm(e3_adj,data=mydata);
res_e4_adj<-lm(e4_adj,data=mydata);
res_e5_adj<-lm(e5_adj,data=mydata);

#print(summary(res_e1_adj));
#print(summary(res_e2_adj));
#print(summary(res_e3_adj));
#print(summary(res_e4_adj));
#print(summary(res_e5_adj));


#simulate
coef_e1<-res_e1_adj$coefficients;
coef_e2<-res_e2_adj$coefficients;
coef_e3<-res_e3_adj$coefficients;
coef_e4<-res_e4_adj$coefficients;
coef_e5<-res_e5_adj$coefficients;

#[real ind f M2 loan], endogenous matrix
mat_A<-matrix(rep(0,0,25),5,5)
mat_A[1,]<-c(1,0,-coef_e1[‘f_fit‘],-coef_e1[‘M2_fit‘],0);
mat_A[2,]<-c(0,1,0,-coef_e2[‘M2_fit‘],-coef_e2[‘loan_fit‘]);
mat_A[3,]<-c(-coef_e3[‘real_fit‘],-coef_e3[‘ind_fit‘],1,0,0);
mat_A[4,]<-c(0,0,-coef_e4[‘f_fit‘],1,-coef_e4[‘loan_fit‘]);
mat_A[5,]<-c(-coef_e5[‘real_fit‘],0,0,0,1);
#[iC ex bot req cb_dep loan_1],exogenous matrix
mat_B<-matrix(0,5,6);
mat_B[1,]<-c(0,0,0,0,0,0);
mat_B[2,]<-c(0,0,0,0,0,coef_e2[‘loan_1‘]);
mat_B[3,]<-c(0,coef_e3[‘ex‘],coef_e3[‘bot_sm‘],0,0,0);
mat_B[4,]<-c(0,0,0,0,0,0);
mat_B[5,]<-c(coef_e5[‘iC‘],0,0,coef_e5[‘req‘],coef_e5[‘cb_dep‘],0);
#const matrix
mat_C<-matrix(0,5,1);
mat_C[1,]<-c(coef_e1[1]);
mat_C[2,]<-c(coef_e2[1]);
mat_C[3,]<-c(coef_e3[1]);
mat_C[4,]<-c(coef_e4[1]);
mat_C[5,]<-c(coef_e5[1]);
#data matrix
mat_data<-matrix(0,76,6);
mat_data[,1]<-mydata$iC;
mat_data[,2]<-mydata$ex;
mat_data[,3]<-mydata$bot;
mat_data[,4]<-mydata$req;
mat_data[,5]<-mydata$cb_dep;
mat_data[,6]<-mydata$loan_1;

mat_fit<-matrix(0,76,5);

for(i in 1:(length(mat_data[,1])))
{
#mat_temp<-matrix(mat_data[i,],8,1);
mat_temp<-matrix(mat_data[i,],6,1);
mat_fit[i,]<-solve(mat_A) %*% (mat_C + mat_B %*% mat_temp);
}

mat_endo<-matrix(0,76,5);
mat_endo[,1]<-mydata$real_sm;
mat_endo[,2]<-mydata$ind;
mat_endo[,3]<-mydata$f;
mat_endo[,4]<-mydata$M2;
mat_endo[,5]<-mydata$loan;

#effect
mat_fit_m<-matrix(0,76,5);
mat_effect<-matrix(0,length(mat_A[1,]),length(mat_data[1,])-1);
#This loop can simulate the system.
#Each simulation will decrease a specified variable to 99%
#while keep others invariable.
for(n in 1:(length(mat_data[1,])-1))#get how many colums
{
temp<-mat_data[,n];
mat_data[,n]<-mat_data[,n]*1.01;

for(i in 1:(length(mat_data[,1])))
{
#mat_temp<-matrix(mat_data[i,],8,1);
mat_temp<-matrix(mat_data[i,],6,1);
mat_fit_m[i,]<-solve(mat_A) %*% (mat_C + mat_B %*% mat_temp);
}
effect_real<-sum(mat_fit_m[,1]-mat_fit[,1])/sum(mat_fit[,1]);
effect_ind<-sum(mat_fit_m[,2]-mat_fit[,2])/sum(mat_fit[,2]);
effect_f<-sum(mat_fit_m[,3]-mat_fit[,3])/sum(mat_fit[,3]);
effect_M2<-sum(mat_fit_m[,4]-mat_fit[,4])/sum(mat_fit[,4]);
effect_loan<-sum(mat_fit_m[,5]-mat_fit[,5])/sum(mat_fit[,5]);
mat_effect[n,]<-
c(effect_real,effect_ind,effect_f,effect_M2,effect_loan);

mat_data[,n]<-temp;
}

print(mat_effect);

#均方根比
#print(sqrt(mean(((mat_fit[,1]-mat_endo[,1])/mat_endo[,1])^2)));
#print(sqrt(mean(((mat_fit[,2]-mat_endo[,2])/mat_endo[,2])^2)));
#print(sqrt(mean(((mat_fit[,3]-mat_endo[,3])/mat_endo[,3])^2)));
#print(sqrt(mean(((mat_fit[,4]-mat_endo[,4])/mat_endo[,4])^2)));
#print(sqrt(mean(((mat_fit[,5]-mat_endo[,5])/mat_endo[,5])^2)));