获取对数因变量的拟合值到 R 中的原始面板数据集
Get fitted values of a log dependent variable to the original panel data set in R
我正在尝试合并对数对数模型因变量的拟合值。我的数据集是一个不平衡的面板。我尝试按照 here 指示的方式进行操作。但我的问题是不同的，因为我已经将我的大数据集转换成一个 plm 对象，它是一个日志因变量。
我的简单数据集可以通过以下代码访问。
dat = structure(list(Time = structure(c(9L, 7L, 15L, 1L, 17L, 13L,
11L, 3L, 23L, 21L, 19L, 5L, 10L, 8L, 16L, 2L, 18L, 14L, 12L,
4L, 24L, 22L, 20L, 6L), .Label = c("Apr-00", "Apr-01", "Aug-00",
"Aug-01", "Dec-00", "Dec-01", "Feb-00", "Feb-01", "Jan-00", "Jan-01",
"Jul-00", "Jul-01", "Jun-00", "Jun-01", "Mar-00", "Mar-01", "May-00",
"May-01", "Nov-00", "Nov-01", "Oct-00", "Oct-01", "Sep-00", "Sep-01"
), class = "factor"), Firm = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), Out = c(161521L,
142452L, 365697L, 355789L, 376843L, 258762L, 255447L, 188545L,
213663L, 273209L, 317468L, 238668L, 241286L, 135288L, 363609L,
318472L, 446279L, 390230L, 118945L, 174887L, 183770L, 197832L,
317468L, 238668L), Lab = c(261L, 334L, 156L, 134L, 159L, 119L,
41L, 247L, 251L, 62L, 525L, 217L, 298L, 109L, 7L, NA, 0L, 50L,
143L, 85L, 80L, 214L, 525L, 217L), Cap = c(13L, 15L, 14L, 12L,
15L, 12L, 45L, 75L, NA, 12L, 15L, 16L, 42L, 45L, 24L, 56L, 12L,
12L, 45L, NA, 15L, 12L, 15L, 16L)), .Names = c("Time", "Firm",
"Out", "Lab", "Cap"), class = "data.frame", row.names = c(NA,
-24L))
我的数据集如下所示，缺少预测变量的数据。
+--------+------+--------+-----+-----+
| Time | Firm | Out | Lab | Cap |
+--------+------+--------+-----+-----+
| Jan-00 | A | 161521 | 261 | 13 |
| Feb-00 | A | 142452 | 334 | 15 |
| Mar-00 | A | 365697 | 156 | 14 |
| Apr-00 | A | 355789 | 134 | 12 |
| May-00 | A | 376843 | 159 | 15 |
| Jun-00 | A | 258762 | 119 | 12 |
| Jul-00 | A | 255447 | 41 | 45 |
| Aug-00 | A | 188545 | 247 | 75 |
| Sep-00 | A | 213663 | 251 | |
| Oct-00 | A | 273209 | 62 | 12 |
| Nov-00 | A | 317468 | 525 | 15 |
| Dec-00 | A | 238668 | 217 | 16 |
| Jan-01 | B | 241286 | 298 | 42 |
| Feb-01 | B | 135288 | 109 | 45 |
| Mar-01 | B | 363609 | 7 | 24 |
| Apr-01 | B | 318472 | | 56 |
| May-01 | B | 446279 | 0 | 12 |
| Jun-01 | B | 390230 | 50 | 12 |
| Jul-01 | B | 118945 | 143 | 45 |
| Aug-01 | B | 174887 | 85 | |
| Sep-01 | B | 183770 | 80 | 15 |
| Oct-01 | B | 197832 | 214 | 12 |
| Nov-01 | B | 317468 | 525 | 15 |
| Dec-01 | B | 238668 | 217 | 16 |
+--------+------+--------+-----+-----+
我可以使用以下代码得到拟合值
library(zoo)
library(plm)
Sys.setlocale("LC_TIME", "English")
dat["time1"] <- as.yearmon(dat$Time,format="%b-%y")
pdat <-pdata.frame(dat,index=c("Firm","time1"))
Model1<- plm(log(Out) ~ lag(log(Cap), 1) + log(Lab + 1),
model = "within", data=pdat)
summary(Model1)
library(data.table)
FV_Log <- data.table(Model1$model[[1]] - Model1$residuals)
但是pdat的观察是24个观察FV_Log是19个观察，所以我无法将它合并到pdat中。我的 pdat 很大，有数千个观察结果，我使用代码创建了许多变量。因此，我们将不胜感激将拟合值正确合并到原始 pdat 中（不更改顺序）的任何帮助。
您面临的问题是双重的。首先，您有 NA 个，您的模型在计算残差时正在下降，因此您的结果自然会包含更少的元素。第二个问题是你滞后于你的一个协变量，它为你的第一次观察（首先是时间）产生一个 NA - 毕竟这就是滞后所做的。要克服该问题，您需要来自前一个时间段的额外数据，否则您必须放弃该观察。假设您无法访问之前时间段的数据，这就是我解决问题的方法。
#First I would create a new variable for CAP and just lag and log that separately, rather than applying the function in the formula of the model itself
pdat$Cap.lag.ln<-lag(log(pdat$Cap), 1)
pdat$Cap<-NULL #deleting the old variable to clear up the mess
#Dont necessarily need the na.omit but it couldn't hurt...
Model1<- plm(log(Out) ~ Cap.lag.ln + log(Lab + 1),
model = "within", data=pdat, na.omit=TRUE)
FV_Log <- data.table(Model1$model[[1]] - Model1$residuals)
#Now this is where you reduce your original dataset (pdat) by getting rid of the NAs
pdat2<-na.omit(pdat)
#You will notice that they're the same dimensions now and you can cbind
pdat3 <-cbind(pdat2,FV_Log)
Time Firm Out Lab time1 Cap.lag.ln V1
1: Feb-00 A 142452 334 Feb 2000 2 12.41211
2: Mar-00 A 365697 156 Mar 2000 2 12.54861
3: Apr-00 A 355789 134 Apr 2000 2 12.57580
4: May-00 A 376843 159 May 2000 2 12.54520
5: Jun-00 A 258762 119 Jun 2000 2 12.59702
6: Jul-00 A 255447 41 Jul 2000 2 12.78611
7: Aug-00 A 188545 247 Aug 2000 3 12.28887
8: Sep-00 A 213663 251 Sep 2000 4 12.10858
9: Nov-00 A 317468 525 Nov 2000 2 12.33084
10: Dec-00 A 238668 217 Dec 2000 2 12.48949
11: Feb-01 B 135288 109 Feb 2001 3 12.20776
12: Mar-01 B 363609 7 Mar 2001 3 12.67984
13: May-01 B 446279 0 May 2001 4 12.87698
14: Jun-01 B 390230 50 Jun 2001 2 12.52360
15: Jul-01 B 118945 143 Jul 2001 2 12.33665
16: Aug-01 B 174887 85 Aug 2001 3 12.25209
17: Oct-01 B 197832 214 Oct 2001 2 12.26445
18: Nov-01 B 317468 525 Nov 2001 2 12.10331
19: Dec-01 B 238668 217 Dec 2001 2 12.26195
如果您想找回那些 NA 的背影，您可以执行以下操作：
pdat3 <-as.data.frame(pdat3)
pdat4<-merge(pdat3, pdat,
by=c("Time","Firm","Out", "Lab","time1"),
all.x=TRUE,all.y=TRUE)
Time Firm Out Lab time1 Cap.lag.ln V1 Cap
1 Apr-00 A 355789 134 Apr 2000 2 12.57580 12
2 Apr-01 B 318472 NA Apr 2001 NA NA 56
3 Aug-00 A 188545 247 Aug 2000 3 12.28887 75
4 Aug-01 B 174887 85 Aug 2001 3 12.25209 NA
5 Dec-00 A 238668 217 Dec 2000 2 12.48949 16
6 Dec-01 B 238668 217 Dec 2001 2 12.26195 16
7 Feb-00 A 142452 334 Feb 2000 2 12.41211 15
8 Feb-01 B 135288 109 Feb 2001 3 12.20776 45
9 Jan-00 A 161521 261 Jan 2000 NA NA 13
10 Jan-01 B 241286 298 Jan 2001 NA NA 42
11 Jul-00 A 255447 41 Jul 2000 2 12.78611 45
12 Jul-01 B 118945 143 Jul 2001 2 12.33665 45
13 Jun-00 A 258762 119 Jun 2000 2 12.59702 12
14 Jun-01 B 390230 50 Jun 2001 2 12.52360 12
15 Mar-00 A 365697 156 Mar 2000 2 12.54861 14
16 Mar-01 B 363609 7 Mar 2001 3 12.67984 24
17 May-00 A 376843 159 May 2000 2 12.54520 15
18 May-01 B 446279 0 May 2001 4 12.87698 12
19 Nov-00 A 317468 525 Nov 2000 2 12.33084 15
20 Nov-01 B 317468 525 Nov 2001 2 12.10331 15
21 Oct-00 A 273209 62 Oct 2000 NA NA 12
22 Oct-01 B 197832 214 Oct 2001 2 12.26445 12
23 Sep-00 A 213663 251 Sep 2000 4 12.10858 NA
24 Sep-01 B 183770 80 Sep 2001 NA NA 15
我正在尝试合并对数对数模型因变量的拟合值。我的数据集是一个不平衡的面板。我尝试按照 here 指示的方式进行操作。但我的问题是不同的，因为我已经将我的大数据集转换成一个 plm 对象，它是一个日志因变量。
我的简单数据集可以通过以下代码访问。
dat = structure(list(Time = structure(c(9L, 7L, 15L, 1L, 17L, 13L,
11L, 3L, 23L, 21L, 19L, 5L, 10L, 8L, 16L, 2L, 18L, 14L, 12L,
4L, 24L, 22L, 20L, 6L), .Label = c("Apr-00", "Apr-01", "Aug-00",
"Aug-01", "Dec-00", "Dec-01", "Feb-00", "Feb-01", "Jan-00", "Jan-01",
"Jul-00", "Jul-01", "Jun-00", "Jun-01", "Mar-00", "Mar-01", "May-00",
"May-01", "Nov-00", "Nov-01", "Oct-00", "Oct-01", "Sep-00", "Sep-01"
), class = "factor"), Firm = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), Out = c(161521L,
142452L, 365697L, 355789L, 376843L, 258762L, 255447L, 188545L,
213663L, 273209L, 317468L, 238668L, 241286L, 135288L, 363609L,
318472L, 446279L, 390230L, 118945L, 174887L, 183770L, 197832L,
317468L, 238668L), Lab = c(261L, 334L, 156L, 134L, 159L, 119L,
41L, 247L, 251L, 62L, 525L, 217L, 298L, 109L, 7L, NA, 0L, 50L,
143L, 85L, 80L, 214L, 525L, 217L), Cap = c(13L, 15L, 14L, 12L,
15L, 12L, 45L, 75L, NA, 12L, 15L, 16L, 42L, 45L, 24L, 56L, 12L,
12L, 45L, NA, 15L, 12L, 15L, 16L)), .Names = c("Time", "Firm",
"Out", "Lab", "Cap"), class = "data.frame", row.names = c(NA,
-24L))
我的数据集如下所示，缺少预测变量的数据。
+--------+------+--------+-----+-----+
| Time | Firm | Out | Lab | Cap |
+--------+------+--------+-----+-----+
| Jan-00 | A | 161521 | 261 | 13 |
| Feb-00 | A | 142452 | 334 | 15 |
| Mar-00 | A | 365697 | 156 | 14 |
| Apr-00 | A | 355789 | 134 | 12 |
| May-00 | A | 376843 | 159 | 15 |
| Jun-00 | A | 258762 | 119 | 12 |
| Jul-00 | A | 255447 | 41 | 45 |
| Aug-00 | A | 188545 | 247 | 75 |
| Sep-00 | A | 213663 | 251 | |
| Oct-00 | A | 273209 | 62 | 12 |
| Nov-00 | A | 317468 | 525 | 15 |
| Dec-00 | A | 238668 | 217 | 16 |
| Jan-01 | B | 241286 | 298 | 42 |
| Feb-01 | B | 135288 | 109 | 45 |
| Mar-01 | B | 363609 | 7 | 24 |
| Apr-01 | B | 318472 | | 56 |
| May-01 | B | 446279 | 0 | 12 |
| Jun-01 | B | 390230 | 50 | 12 |
| Jul-01 | B | 118945 | 143 | 45 |
| Aug-01 | B | 174887 | 85 | |
| Sep-01 | B | 183770 | 80 | 15 |
| Oct-01 | B | 197832 | 214 | 12 |
| Nov-01 | B | 317468 | 525 | 15 |
| Dec-01 | B | 238668 | 217 | 16 |
+--------+------+--------+-----+-----+
我可以使用以下代码得到拟合值
library(zoo)
library(plm)
Sys.setlocale("LC_TIME", "English")
dat["time1"] <- as.yearmon(dat$Time,format="%b-%y")
pdat <-pdata.frame(dat,index=c("Firm","time1"))
Model1<- plm(log(Out) ~ lag(log(Cap), 1) + log(Lab + 1),
model = "within", data=pdat)
summary(Model1)
library(data.table)
FV_Log <- data.table(Model1$model[[1]] - Model1$residuals)
但是pdat的观察是24个观察FV_Log是19个观察，所以我无法将它合并到pdat中。我的 pdat 很大，有数千个观察结果，我使用代码创建了许多变量。因此，我们将不胜感激将拟合值正确合并到原始 pdat 中（不更改顺序）的任何帮助。
您面临的问题是双重的。首先，您有 NA 个，您的模型在计算残差时正在下降，因此您的结果自然会包含更少的元素。第二个问题是你滞后于你的一个协变量，它为你的第一次观察（首先是时间）产生一个 NA - 毕竟这就是滞后所做的。要克服该问题，您需要来自前一个时间段的额外数据，否则您必须放弃该观察。假设您无法访问之前时间段的数据，这就是我解决问题的方法。
#First I would create a new variable for CAP and just lag and log that separately, rather than applying the function in the formula of the model itself
pdat$Cap.lag.ln<-lag(log(pdat$Cap), 1)
pdat$Cap<-NULL #deleting the old variable to clear up the mess
#Dont necessarily need the na.omit but it couldn't hurt...
Model1<- plm(log(Out) ~ Cap.lag.ln + log(Lab + 1),
model = "within", data=pdat, na.omit=TRUE)
FV_Log <- data.table(Model1$model[[1]] - Model1$residuals)
#Now this is where you reduce your original dataset (pdat) by getting rid of the NAs
pdat2<-na.omit(pdat)
#You will notice that they're the same dimensions now and you can cbind
pdat3 <-cbind(pdat2,FV_Log)
Time Firm Out Lab time1 Cap.lag.ln V1
1: Feb-00 A 142452 334 Feb 2000 2 12.41211
2: Mar-00 A 365697 156 Mar 2000 2 12.54861
3: Apr-00 A 355789 134 Apr 2000 2 12.57580
4: May-00 A 376843 159 May 2000 2 12.54520
5: Jun-00 A 258762 119 Jun 2000 2 12.59702
6: Jul-00 A 255447 41 Jul 2000 2 12.78611
7: Aug-00 A 188545 247 Aug 2000 3 12.28887
8: Sep-00 A 213663 251 Sep 2000 4 12.10858
9: Nov-00 A 317468 525 Nov 2000 2 12.33084
10: Dec-00 A 238668 217 Dec 2000 2 12.48949
11: Feb-01 B 135288 109 Feb 2001 3 12.20776
12: Mar-01 B 363609 7 Mar 2001 3 12.67984
13: May-01 B 446279 0 May 2001 4 12.87698
14: Jun-01 B 390230 50 Jun 2001 2 12.52360
15: Jul-01 B 118945 143 Jul 2001 2 12.33665
16: Aug-01 B 174887 85 Aug 2001 3 12.25209
17: Oct-01 B 197832 214 Oct 2001 2 12.26445
18: Nov-01 B 317468 525 Nov 2001 2 12.10331
19: Dec-01 B 238668 217 Dec 2001 2 12.26195
如果您想找回那些 NA 的背影，您可以执行以下操作：
pdat3 <-as.data.frame(pdat3)
pdat4<-merge(pdat3, pdat,
by=c("Time","Firm","Out", "Lab","time1"),
all.x=TRUE,all.y=TRUE)
Time Firm Out Lab time1 Cap.lag.ln V1 Cap
1 Apr-00 A 355789 134 Apr 2000 2 12.57580 12
2 Apr-01 B 318472 NA Apr 2001 NA NA 56
3 Aug-00 A 188545 247 Aug 2000 3 12.28887 75
4 Aug-01 B 174887 85 Aug 2001 3 12.25209 NA
5 Dec-00 A 238668 217 Dec 2000 2 12.48949 16
6 Dec-01 B 238668 217 Dec 2001 2 12.26195 16
7 Feb-00 A 142452 334 Feb 2000 2 12.41211 15
8 Feb-01 B 135288 109 Feb 2001 3 12.20776 45
9 Jan-00 A 161521 261 Jan 2000 NA NA 13
10 Jan-01 B 241286 298 Jan 2001 NA NA 42
11 Jul-00 A 255447 41 Jul 2000 2 12.78611 45
12 Jul-01 B 118945 143 Jul 2001 2 12.33665 45
13 Jun-00 A 258762 119 Jun 2000 2 12.59702 12
14 Jun-01 B 390230 50 Jun 2001 2 12.52360 12
15 Mar-00 A 365697 156 Mar 2000 2 12.54861 14
16 Mar-01 B 363609 7 Mar 2001 3 12.67984 24
17 May-00 A 376843 159 May 2000 2 12.54520 15
18 May-01 B 446279 0 May 2001 4 12.87698 12
19 Nov-00 A 317468 525 Nov 2000 2 12.33084 15
20 Nov-01 B 317468 525 Nov 2001 2 12.10331 15
21 Oct-00 A 273209 62 Oct 2000 NA NA 12
22 Oct-01 B 197832 214 Oct 2001 2 12.26445 12
23 Sep-00 A 213663 251 Sep 2000 4 12.10858 NA
24 Sep-01 B 183770 80 Sep 2001 NA NA 15