具有横截面平均值的面板回归
Panel regression with cross sectional averages
我正在估计面板回归模型，我需要将因变量和回归变量的横截面平均值添加到模型中。
我正在努力在 R 中实现横截面平均值。任何人都可以帮助我。
所以我在下面有一个面板回归代码 - 使用 plm 包。
我需要将变量 A、B、C 和 D 的横截面平均值添加到回归的右侧
library(plm)
panel_fe <- plm(A ~ B+ C + D, model = "fd", effect="individual", data = PanelS)
所以我最终的回归模型是这样的 A = B+ C+D + A_bar + B_bar + C_bar + D_bar，其中 A_bar、B_bar、C_bar、D_bar分别为A、B、C、D的截面平均值。
我的面板数据集如下，PanelS。
structure(list(Country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L), .Label = c("CountryA", "CountryB",
"CountryC", "CountryD", "CountryE", "CountryF", "CountryG", "CountryH",
"CountryI", "CountryJ"), class = "factor"), Year = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L,
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19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L,
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20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L), .Label = c("2000", "2001",
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"2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
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"CountryI-2005", "CountryI-2006", "CountryI-2007", "CountryI-2008",
"CountryI-2009", "CountryI-2010", "CountryI-2011", "CountryI-2012",
"CountryI-2013", "CountryI-2014", "CountryI-2015", "CountryI-2016",
"CountryI-2017", "CountryI-2018", "CountryI-2019", "CountryJ-2000",
"CountryJ-2001", "CountryJ-2002", "CountryJ-2003", "CountryJ-2004",
"CountryJ-2005", "CountryJ-2006", "CountryJ-2007", "CountryJ-2008",
"CountryJ-2009", "CountryJ-2010", "CountryJ-2011", "CountryJ-2012",
"CountryJ-2013", "CountryJ-2014", "CountryJ-2015", "CountryJ-2016",
"CountryJ-2017", "CountryJ-2018", "CountryJ-2019"), class = c("pdata.frame",
"data.frame"), index = structure(list(Country = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), .Label = c("CountryA",
"CountryB", "CountryC", "CountryD", "CountryE", "CountryF", "CountryG",
"CountryH", "CountryI", "CountryJ"), class = "factor"), Year = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L), .Label = c("2000", "2001",
"2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018", "2019"), class = "factor")), class = c("pindex", "data.frame"
), row.names = c(NA, 200L)))
您可以使用包 plm 中的函数 Between 来计算横截面平均值并将它们添加到您的数据中：
library(plm)
# PanelS is a pdata.frame (otherwise use pdata.frame(your_data, index))
PanelS$A_bar <- Between(PanelS$A)
PanelS$B_bar <- Between(PanelS$B)
PanelS$C_bar <- Between(PanelS$C)
PanelS$D_bar <- Between(PanelS$D)
mod <- plm(A ~ B + C + D + A_bar + B_bar + C_bar + D_bar, model = "pooling", effect="individual", data = PanelS)
summary(mod)
# Pooling Model
#
# Call:
# plm(formula = A ~ B + C + D + A_bar + B_bar + C_bar + D_bar,
# data = PanelS, effect = "individual", model = "pooling")
#
# Balanced Panel: n = 10, T = 20, N = 200
#
# Residuals:
# Min. 1st Qu. Median 3rd Qu. Max.
# -0.06143690 -0.01311792 0.00070253 0.01186605 0.05107105
#
# Coefficients:
# Estimate Std. Error t-value Pr(>|t|)
# (Intercept) -0.00000000000001042 0.03313743211380626 0.0000 1.000000
# B -0.00076930351859426 0.00020566635571130 -3.7405 0.000242 ***
# C 0.10827039012266901 0.00949296134830719 11.4053 < 0.00000000000000022 ***
# D -0.04222788490989914 0.01136058813979121 -3.7171 0.000264 ***
# A_bar 0.99999999999911215 0.09632471140222754 10.3816 < 0.00000000000000022 ***
# C_bar -0.10827039012256123 0.01033406661607372 -10.4770 < 0.00000000000000022 ***
# D_bar 0.04222788490990802 0.03874710199411169 1.0898 0.277145
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Total Sum of Squares: 0.17549
# Residual Sum of Squares: 0.07128
# R-Squared: 0.59382
# Adj. R-Squared: 0.58119
# F-statistic: 47.0268 on 6 and 193 DF, p-value: < 0.000000000000000222
请注意，您似乎想要估计固定效应模型，但您的估计 model = "fd" 可以估计一阶差分模型。另请注意，横截面平均值将退出固定效应模型的估计。
我正在估计面板回归模型，我需要将因变量和回归变量的横截面平均值添加到模型中。 我正在努力在 R 中实现横截面平均值。任何人都可以帮助我。
所以我在下面有一个面板回归代码 - 使用 plm 包。 我需要将变量 A、B、C 和 D 的横截面平均值添加到回归的右侧
library(plm)
panel_fe <- plm(A ~ B+ C + D, model = "fd", effect="individual", data = PanelS)
所以我最终的回归模型是这样的 A = B+ C+D + A_bar + B_bar + C_bar + D_bar，其中 A_bar、B_bar、C_bar、D_bar分别为A、B、C、D的截面平均值。
我的面板数据集如下，PanelS。
structure(list(Country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L), .Label = c("CountryA", "CountryB",
"CountryC", "CountryD", "CountryE", "CountryF", "CountryG", "CountryH",
"CountryI", "CountryJ"), class = "factor"), Year = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L), .Label = c("2000", "2001",
"2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018", "2019"), class = "factor"), A = c(0.051539, 0.064525,
0.014292, 0.018774, 0.035449, 0.021988, 0.02396, 0.011415, 0.010358,
-0.029607, -0.020427, -0.012734, 0.006683, 0.007373, -0.039712,
-0.005499, 0.008682, 0.015326, 0.020524, 0.015101, 0.035355,
0.031157, 0.023387, 0.024198, 0.035353, 0.053873, 0.038743, 0.042338,
0.034935, 0.015377, 0.010599, 0.015154, 0.002919, 0.024291, 0.043819,
0.015901, 0.01897, 0.027767, 0.015992, 0.041976, 0.011223, 0.006144,
0.000778, 0.005873, 0.007194, -0.022017, -0.023338, -0.037765,
-0.049356, 0.026135, 0.035633, 0.015691, -0.006196, -0.00025,
0.001181, -0.001472, -0.009324, -0.022664, -0.022623, -0.019586,
-0.012207, -0.004603, -0.013073, -0.010771, -0.009882, -0.014417,
-0.031812, -0.043885, -0.050883, -0.039834, -0.020299, -0.000684,
0.011216, 0.005419, 0.000939, -0.005508, 0.006266, -0.008077,
-0.016137, -0.012681, 0.031612, 0.043729, 0.009314, 0.002734,
-0.012284, 0.002403, 0.016807, 0.019995, 0.033096, 0.024383,
0.010588, 0.019833, 0.031837, 0.03127, 0.029059, 0.020708, 0.019296,
0.017787, 0.032074, 0.027125, 0.005673, 0.003698, -5.3e-05, 0.001794,
-0.011977, -0.008686, -0.031588, -0.039411, -0.073931, -0.076715,
-0.039171, -0.025797, -0.007637, 0.00345, 0.009101, 0.01674,
-0.006968, -0.019178, -0.02438, -0.039663, 0.078313, 0.06707,
0.062822, 0.050771, 0.041274, 0.043921, 0.046429, 0.039418, 0.034671,
0.017356, 0.001054, 0.00414, 0.00226, 0.00275, 0.00085, 0.00495,
0.001276, -0.001446, -0.005771, -0.007513, 0.053734, 0.038679,
0.017375, 0.01438, 0.018403, 0.032943, 0.025539, 0.032463, 0.032267,
0.034009, 0.018229, 0.008958, 0.010079, 0.00749, 0.000604, 0.001948,
0.011782, 0.013253, 0.007898, 0.007546, 0.018052, -0.001123,
-0.012597, -0.042292, -0.058516, -0.022736, -0.03841, -0.050843,
-0.073979, -0.097242, -0.024712, 0.038037, 0.048685, -0.00624,
0.075575, 0.044947, 0.097171, 0.086809, 0.079856, 0.068521, 0.008062,
-0.00911, -0.010527, -4.3e-05, 0.002428, 0.004422, 0.008752,
0.019602, 0.01724, 0.01965, -0.008816, 0.011466, 0.020956, 0.021873,
0.021772, 0.024495, 0.021354, 0.015267, 0.018769, 0.016904),
C = c(0.75345, 0.70657, 0.645051, 0.510055, 0.433786, 0.35728,
0.265817, 0.208721, 0.163261, 0.130248, 0.136607, 0.153873,
0.152275, 0.166592, 0.170559, 0.27089, 0.259813, 0.292847,
0.253142, 0.222618, 0.56764082, 0.523543, 0.485083, 0.49081,
0.461501, 0.44156, 0.374122, 0.315494, 0.27346, 0.333132,
0.401818, 0.425879, 0.460709, 0.448942, 0.440456, 0.442703,
0.397737, 0.372338, 0.359446, 0.340254, 0.064305, 0.05107,
0.047682, 0.056584, 0.055981, 0.051134, 0.047025, 0.046318,
0.037655, 0.045041, 0.071989, 0.066074, 0.061057, 0.097641,
0.101621, 0.105545, 0.09996, 0.099131, 0.091119, 0.082012,
0.120817, 0.120871, 0.138383, 0.13023, 0.141247, 0.146088,
0.119133, 0.100396, 0.084592, 0.185873, 0.368416, 0.479167,
0.4367, 0.421837, 0.400428, 0.416259, 0.37072, 0.40398, 0.390126,
0.371126, 0.079576, 0.074647, 0.076712, 0.074295, 0.074504,
0.079053, 0.080224, 0.082991, 0.082006, 0.15357, 0.161465,
0.201522, 0.190049, 0.219974, 0.236873, 0.227428, 0.219862,
0.200938, 0.223426, 0.209529, 0.217219, 0.224867, 0.258694,
0.248207, 0.221093, 0.189452, 0.159052, 0.124236, 0.119492,
0.123362, 0.217807, 0.296186, 0.339882, 0.371345, 0.376212,
0.391509, 0.378059, 0.373931, 0.351043, 0.347354, 0.440547,
0.424547, 0.409236, 0.401795, 0.427482, 0.426416, 0.399297,
0.381117, 0.339041, 0.325607, 0.415314, 0.469047, 0.482712,
0.536225, 0.562292, 0.598259, 0.636417, 0.631764, 0.612668,
0.596271, 0.605061, 0.503479, 0.518971, 0.498057, 0.492731,
0.484527, 0.486885, 0.43596, 0.388967, 0.374978, 0.407324,
0.381025, 0.371731, 0.375149, 0.402248, 0.449982, 0.437387,
0.422554, 0.407331, 0.389125, 0.989067, 1.049344, 1.070812,
1.048631, 1.014561, 1.028734, 1.073949, 1.036117, 1.03103,
1.094155, 1.267447, 1.474942, 1.752192, 1.619444, 1.784347,
1.802256, 1.770079, 1.807951, 1.792139, 1.862386, 0.601394,
0.590658, 0.579365, 0.597035, 0.633089, 0.649877, 0.673465,
0.667047, 0.639942, 0.655222, 0.729901, 0.823816, 0.79801,
0.811354, 0.787169, 0.756694, 0.72207, 0.692768, 0.651024,
0.617801), B = c(0.147502302, 0.043680673, -0.212478849,
-0.266834333, -0.228099071, -0.199890362, -0.968175801, 1.047500546,
1.273127656, 1.227657506, -0.286068921, -1.356896168, -1.442625298,
-0.291748363, 2.029875219, 1.099611751, -1.112127832, -0.894025857,
0.103213651, 0.286801553, 0.756833023, 0.591945192, 0.525259532,
0.466656359, 0.706692697, -2.361722697, -2.777257989, -4.097114222,
-4.564987155, 2.317853991, 3.44030537, 3.034469093, 5.845290721,
0.403542521, 0.128582254, 0.817094156, -0.886707561, -2.998573025,
-0.491794488, -0.856367773, 0.023343476, -0.209503364, -0.084839186,
-0.146285026, -0.256672799, -0.093852713, 0.145824486, 0.434606031,
0.966980327, 0.67904687, -0.292659443, -0.487763914, -0.084930583,
-0.32722087, -0.442172133, -0.168366978, -0.186469629, 0.046322287,
0.181126569, 0.303486593, 0.171541123, -0.348150815, -0.407466419,
-0.624622679, -0.354132366, -0.15050691, 0.700892294, 0.67692383,
1.014111655, 0.862019536, 0.395600738, -0.256706715, -0.542246369,
-0.539422399, -0.405088653, -0.247954994, -0.497333992, -0.010723655,
0.393516751, 0.169750037, -0.581903347, -0.730163914, 0.351894514,
0.629568917, 0.882078894, 0.760041333, -0.564317727, -0.57799292,
-0.433736512, 0.513350369, 0.55464973, -0.224497194, -0.074326596,
-0.123301819, -0.432013928, -0.25316664, -0.374406673, 0.116449941,
0.308969388, 0.252824183, 2.398228162, -0.033362631, -1.681378615,
-3.655293426, -2.793256764, -3.636310622, 0.149490332, 3.951131246,
7.177449077, 4.831325877, 2.050070679, 1.314471427, -1.687424783,
-3.796189127, -3.329685346, -1.695252718, -3.010416797, -2.414597902,
1.199960369, 4.661041564, 0.531518012, -1.384184059, -0.64216453,
-0.13206166, 0.249287935, -0.153010531, -0.987952985, -1.71711917,
-0.678751076, 0.890062065, 1.663691535, 1.883735194, 2.171029985,
2.383501603, 1.490313839, -0.732542129, -0.291797363, -1.655272704,
-1.613245217, -1.275038743, -0.789256935, -3.589249982, 0.502475039,
1.840081099, 1.141218417, 3.130100399, 3.94751837, 0.97811035,
0.013586974, -3.245960526, -2.068241886, -1.82476664, -1.481654499,
0.37039449, -1.516414277, -1.722381744, 0.683458083, 0.153189319,
3.410781995, 0.067011953, -3.09418792, -4.09753755, -4.682167411,
-1.333607727, 2.505605899, -4.332639317, -2.190945016, 4.048457741,
11.60535564, 13.61047901, 5.145259686, -0.712611552, -3.385649938,
7.214394614, -10.34401695, -1.841542179, -6.437949187, -4.545422837,
-0.012548047, 2.881273043, 3.227611639, 10.96399365, 16.38843255,
14.72001327, -13.84595255, -10.51570643, -13.59695535, -36.70577424,
-12.07070647, 12.51742535, 52.88207865, 9.143152612, -7.818895359,
-15.57456939, -21.31957866, -23.55720863, -5.574415019, 5.783084584,
12.02189272, 22.93207708), D = c(0.77780751, 0.793229898,
0.80623893, 0.821155065, 0.836880111, 0.854312944, 0.873660631,
0.890537317, 0.907536298, 0.912375095, 0.929637942, 0.946439284,
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您可以使用包 plm 中的函数 Between 来计算横截面平均值并将它们添加到您的数据中：
library(plm)
# PanelS is a pdata.frame (otherwise use pdata.frame(your_data, index))
PanelS$A_bar <- Between(PanelS$A)
PanelS$B_bar <- Between(PanelS$B)
PanelS$C_bar <- Between(PanelS$C)
PanelS$D_bar <- Between(PanelS$D)
mod <- plm(A ~ B + C + D + A_bar + B_bar + C_bar + D_bar, model = "pooling", effect="individual", data = PanelS)
summary(mod)
# Pooling Model
#
# Call:
# plm(formula = A ~ B + C + D + A_bar + B_bar + C_bar + D_bar,
# data = PanelS, effect = "individual", model = "pooling")
#
# Balanced Panel: n = 10, T = 20, N = 200
#
# Residuals:
# Min. 1st Qu. Median 3rd Qu. Max.
# -0.06143690 -0.01311792 0.00070253 0.01186605 0.05107105
#
# Coefficients:
# Estimate Std. Error t-value Pr(>|t|)
# (Intercept) -0.00000000000001042 0.03313743211380626 0.0000 1.000000
# B -0.00076930351859426 0.00020566635571130 -3.7405 0.000242 ***
# C 0.10827039012266901 0.00949296134830719 11.4053 < 0.00000000000000022 ***
# D -0.04222788490989914 0.01136058813979121 -3.7171 0.000264 ***
# A_bar 0.99999999999911215 0.09632471140222754 10.3816 < 0.00000000000000022 ***
# C_bar -0.10827039012256123 0.01033406661607372 -10.4770 < 0.00000000000000022 ***
# D_bar 0.04222788490990802 0.03874710199411169 1.0898 0.277145
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Total Sum of Squares: 0.17549
# Residual Sum of Squares: 0.07128
# R-Squared: 0.59382
# Adj. R-Squared: 0.58119
# F-statistic: 47.0268 on 6 and 193 DF, p-value: < 0.000000000000000222
请注意，您似乎想要估计固定效应模型，但您的估计 model = "fd" 可以估计一阶差分模型。另请注意，横截面平均值将退出固定效应模型的估计。