我需要对面板数据进行回归。它有 3 个维度(年份 * 公司 * 国家/地区)。例如:
============================================
year | comp | count | value.x | value.y
------+------+-------+----------+-----------
2000 | A | USA | 1029.0 | 239481
------+------+-------+----------+-----------
2000 | A | CAN | 2341.4 | 129333
------+------+-------+----------+-----------
2000 | B | USA | 2847.7 | 187319
------+------+-------+----------+-----------
2000 | B | CAN | 4820.5 | 392039
------+------+-------+----------+-----------
2001 | A | USA | 7289.9 | 429481
------+------+-------+----------+-----------
2001 | A | CAN | 5067.3 | 589143
------+------+-------+----------+-----------
2001 | B | USA | 7847.8 | 958234
------+------+-------+----------+-----------
2001 | B | CAN | 9820.0 | 1029385
============================================
然而,R 包plm
似乎无法应对超过二维的情况。
我努力了
result <- plm(value.y ~ value.x, data = dataname, index = c("comp","count","year"))
它返回错误:
Error in pdata.frame(data, index) :
'index' can be of length 2 at the most (one individual and one time index)
当面板数据(个体 * 时间)在“个体”内具有超过 1 个维度时,如何运行回归?
如果有人遇到同样的情况,我将我的解决方案放在这里:
R似乎无法应对这种情况。你唯一能做的就是添加假人。如果您添加虚拟变量所依据的分类变量包含太多类别,您可以尝试以下操作:
makedummy <- function(colnum,data,interaction = FALSE,interation_varnum)
{
char0 = colnames(data)[colnum]
char1 = "dummy"
tmp = unique(data[,colnum])
valname = paste(char0,char1,tmp,sep = ".")
valname_int = paste(char0,char1,"int",tmp,sep = ".")
for(i in 1:(length(tmp)-1))
{
if(!interaction)
{
tmp_dummy <- ifelse(data[,colnum]==tmp[i],1,0)
}
if(interaction)
{
index = apply(as.matrix(data[,colnum]),1,identical,y = tmp[i])
tmp_dummy = c()
tmp_dummy[index] = data[index,interation_varnum]
tmp_dummy[!index] = 0
}
tmp_dummy <- data.frame(tmp_dummy)
if(!interaction)
{
colnames(tmp_dummy) <- valname[i]
}
if(interaction)
{
colnames(tmp_dummy) <- valname_int[i]
}
data<-cbind(data,tmp_dummy)
}
return(data)
}
例如:
## Create fake data
fakedata <- matrix(rnorm(300),nrow = 100)
cate <- LETTERS[sample(seq(1,10),100, replace = TRUE)]
fakedata <- cbind.data.frame(cate,fakedata)
## Try this
fakedata <- makedummy(1,fakedata)
## If you need to add dummy*x to see if there is any influences of different categories on the coefficients, try this
fakedata <- makedummy(1,fakedata,interaction = TRUE,interaction_varnum = 2)
这里可能有点啰嗦,我没有润色。欢迎任何建议。现在您可以对数据执行 OLS。