rm(list=ls()) k <- 1000 n <- 100 alp <- 0.05 test.res <- numeric(k) for(i in 1:k){ x1<- rnorm(n,0,0.5) x2<- rbinom(n,1,prob=0.5) y <- 2*x1+3*x2 eb <- rnorm(n,0,1) beta1hat <- solve(t(x1)%*%x1)%*%t(x1)%*%y beta2hat <- solve(t(x1)%*%x1)%*%t(x1)%*%y } hlo1 <- beta1hat-sigma*solve(t(x1)%*%x1)*qnorm(0.025) hup1 <- beta1hat+sigma*solve(t(x1)%*%x1)*qnorm(0.025) hlo2 <- beta2hat-sigma*solve(t(x2)%*%x2)*qnorm(0.025) hup2 <- beta2hat+sigma*solve(t(x2)%*%x2)*qnorm(0.025) 报错

这段代码存在一些问题，导致运行时出现报错。其中的sigma没有定义，应该是指模型误差的标准差，需要在代码中进行定义。同时，最小二乘法估计beta2hat的时候，应该是用t(x2)而不是t(x1)进行计算。修改后的代码如下：

rm(list=ls()) 
k <- 1000 
n <- 100 
alp <- 0.05 
test.res <- numeric(k) 
sigma <- 1 # 定义模型误差的标准差
for(i in 1:k){ 
  x1<- rnorm(n,0,0.5) 
  x2<- rbinom(n,1,prob=0.5) 
  y <- 2*x1+3*x2 
  eb <- rnorm(n,0,1) 
  beta1hat <- solve(t(x1)%*%x1)%*%t(x1)%*%y 
  beta2hat <- solve(t(x2)%*%x2)%*%t(x2)%*%y 
} 
hlo1 <- beta1hat-sigma*solve(t(x1)%*%x1)*qnorm(0.025) 
hup1 <- beta1hat+sigma*solve(t(x1)%*%x1)*qnorm(0.025)   
hlo2 <- beta2hat-sigma*solve(t(x2)%*%x2)*qnorm(0.025) 
hup2 <- beta2hat+sigma*solve(t(x2)%*%x2)*qnorm(0.025) 

这样修改后，代码应该能够正常运行了。