R -plm-内部效应模型和随机效应模型中的误差(合并,第一次差异之间起作用) [英] R -plm - error within and random effects models (pooling, between & first differences work)
问题描述
我对内部"和随机效果方法有疑问(这不起作用).而且我对合并池,第一个差值估计器或第一个差值估计器没有问题->它起作用.
I have problem with Within and random effect method (it doesn't work). And I have no problem with pooling, between or first diffeences estimator -> it works.
我遇到与 R相同的问题-类(x)中的错误-plm-仅在随机效应模型内和. 这是指向我的数据的链接: https://www.dropbox. com/s/8tgeyhxeb0wrdri/my_data.xlsx?raw = 1 (某些国家/地区有一些财务指标和GDP增长)
I have the same problem like R - Error in class(x) - plm - only within and random effects models. Here is the link to my data: https://www.dropbox.com/s/8tgeyhxeb0wrdri/my_data.xlsx?raw=1 (there are some financial measures and GDP growth for some countries)
我的代码:
proba<-read_excel("my_data.xlsx")
attach(proba)
Y<-cbind(GDP_growth)
X<-cbind(gfdddi01, gfdddi02, gfdddi04, gfdddi05)
pdata<-pdata.frame(proba,index=c("id","year"))
##POOLED OLS estimator
pooling<-plm(Y~X,data=pdata,model="pooling")
summary(pooling)
##BETWEEN ESTIMATOR
between<-plm(Y~X,data=pdata,model="between")
summary(between)
#FIRST DIFFERENCES ESTIMATOR
firstdiff<-plm(Y~X,data=pdata,model="fd")
summary(firstdiff)
#FIXED EFFECT OR WITHIN ESTIMATOR
fixed <-plm(Y~X,data=pdata,model="within")
summary(fixed)
#RANDOM EFFECTS ESTIMATOR
random<- plm(Y~X,data=pdata,model="random")
summary(random)
我收到的错误消息:
class(x)中的错误<-setdiff(class(x),"pseries"):除非Dimension属性的长度为2(为0),否则将类设置为矩阵无效
Error in class(x) <- setdiff(class(x), "pseries") : invalid to set the class to matrix unless the dimension attribute is of length 2 (was 0)
有什么问题吗?
推荐答案
请勿使用环境中的变量(就像您对Y
和X
所做的操作一样-无需创建它们).而是在plm
的formula
参数中使用变量名称,因为它们出现在数据pdata
中:
Do not use variables from the environment (like you have done with Y
and X
- no need to create those). Rather, use in the formula
argument of plm
the variable names as they occur in your data pdata
:
#FIXED EFFECT OR WITHIN ESTIMATOR
fixed <-plm(GDP_growth ~ gfdddi01 + gfdddi02 + gfdddi04 + gfdddi05, data = pdata, model ="within")
summary(fixed)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = GDP_growth ~ gfdddi01 + gfdddi02 + gfdddi04 + gfdddi05,
## data = pdata, model = "within")
##
## Balanced Panel: n = 17, T = 41, N = 697
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.89148 -1.17470 0.12701 1.48874 20.70109
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## gfdddi01 -0.0066663 0.0153800 -0.4334 0.6648
## gfdddi02 0.0051626 0.0153343 0.3367 0.7365
## gfdddi04 -0.0245573 0.0150069 -1.6364 0.1022
## gfdddi05 -0.0049627 0.0073786 -0.6726 0.5014
##
## Total Sum of Squares: 5421.5
## Residual Sum of Squares: 5366.8
## R-Squared: 0.010095
## Adj. R-Squared: -0.019192
## F-statistic: 1.72352 on 4 and 676 DF, p-value: 0.14296
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