第９章 パネルデータ（多時点）に親しむ
分析結果の解釈については、書籍をご参照ください。以下はWindows10、R version 4.2.1で実行しています。
データを読み込むにあたって、作業場所の指定とそこにデータが置いてあることが前提になります。 R本体を操作している場合は、「ファイル」→「ディレクトリの変更」でデータの置いてある場所を指定するのが簡単です。 RStudioを操作している場合は、例えばデスクトップの「R」という名前のフォルダにデータがあるとすると
setwd("C:/Users/ユーザー名/Desktop/R")を最初に実行するのがよいでしょう。ユーザー名は各自異なるので注意です。
使用するデータを読み込み、表示します。なお、R version 4.1以前で読み込む際には
data1 <- read.csv("ファイル名.csv", fileEncoding = "UTF-8-BOM")のように、エンコードのオプションを指定する必要があります。version 4.2以降では必要ありませんので、以下ではオプションを指定していません。
data1 <- read.csv("ch9.csv")本章で使用するパッケージを読み込みますが、パッケージはインストールしておく必要があります。 以下のようにコマンドでインストールするか、RやRStudioからクリックでインストールすることもできます。
install.packages(“stargazer”, dependencies = TRUE)パッケージの機能が使えるように読み込みます。
library(ggplot2)
library(plm)
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer表９－１ ５時点のパネルデータ
data1## pref year suic unemp alone popd rain
## 1 1 1995 16.8 4.4 27.88 261.6 12.41
## 2 2 1995 20.1 5.0 21.88 469.3 12.31
## 3 3 1995 24.3 3.2 22.46 387.7 14.15
## 4 4 1995 14.7 3.9 26.89 754.7 9.75
## 5 5 1995 31.8 3.4 18.63 386.2 19.07
## 6 6 1995 21.9 2.7 17.56 439.9 12.06
## 7 7 1995 18.9 3.4 20.72 516.9 11.03
## 8 8 1995 17.0 3.8 19.77 755.1 12.54
## 9 9 1995 18.7 3.7 20.71 687.5 14.03
## 10 10 1995 19.6 3.7 19.96 887.5 10.76
## 11 11 1995 15.6 4.4 21.45 2661.3 11.98
## 12 12 1995 14.6 4.3 24.09 1680.7 10.95
## 13 13 1995 16.2 4.9 38.12 8532.6 12.20
## 14 14 1995 14.8 4.6 28.34 5747.3 14.40
## 15 15 1995 23.9 2.7 19.65 545.4 20.78
## 16 16 1995 19.7 2.8 17.66 608.8 24.28
## 17 17 1995 16.0 3.3 25.53 851.2 26.54
## 18 18 1995 16.7 2.5 19.35 782.8 25.88
## 19 19 1995 18.1 3.4 22.53 936.2 8.45
## 20 20 1995 17.8 2.5 21.35 667.7 9.80
## 21 21 1995 16.5 3.2 18.50 1010.4 17.18
## 22 22 1995 14.2 3.5 21.30 1381.5 16.18
## 23 23 1995 15.0 3.7 25.11 2362.8 13.93
## 24 24 1995 16.2 3.4 20.11 928.2 16.66
## 25 25 1995 13.8 3.1 19.60 997.7 16.82
## 26 26 1995 16.4 4.4 29.00 2328.0 13.66
## 27 27 1995 16.0 6.2 27.44 6777.5 13.79
## 28 28 1995 16.8 5.1 22.37 2022.6 11.91
## 29 29 1995 13.5 4.2 17.71 1717.1 12.87
## 30 30 1995 21.2 4.5 20.07 992.5 14.11
## 31 31 1995 19.8 3.0 19.75 696.6 20.87
## 32 32 1995 25.0 2.4 20.89 595.8 17.84
## 33 33 1995 13.1 3.7 23.18 888.1 10.28
## 34 34 1995 17.9 3.7 26.33 1301.3 13.90
## 35 35 1995 19.3 3.6 24.51 913.1 19.11
## 36 36 1995 16.1 4.5 21.81 829.3 12.26
## 37 37 1995 15.6 3.9 21.93 1046.5 9.99
## 38 38 1995 16.8 4.4 24.12 907.2 13.93
## 39 39 1995 19.8 5.4 26.89 703.4 19.09
## 40 40 1995 16.6 5.5 27.62 1806.1 15.93
## 41 41 1995 15.9 3.5 19.45 652.8 18.57
## 42 42 1995 16.8 4.2 23.46 942.8 15.45
## 43 43 1995 16.6 4.2 23.25 697.6 18.76
## 44 44 1995 17.4 3.9 24.42 695.5 13.09
## 45 45 1995 25.4 4.2 23.85 643.2 20.42
## 46 46 1995 21.7 4.1 27.72 544.4 27.58
## 47 47 1995 19.5 10.3 21.94 1144.9 17.63
## 48 1 2000 26.7 4.8 29.95 259.5 14.45
## 49 2 2000 27.5 5.4 24.08 460.7 14.06
## 50 3 2000 32.2 4.0 24.47 381.7 14.18
## 51 4 2000 23.0 4.9 28.59 755.7 11.87
## 52 5 2000 38.5 4.3 21.24 377.0 15.63
## 53 6 2000 26.1 3.3 19.98 436.5 11.65
## 54 7 2000 23.6 4.3 22.60 504.2 12.91
## 55 8 2000 24.0 4.2 21.42 750.9 14.00
## 56 9 2000 25.6 4.1 22.42 680.5 16.34
## 57 10 2000 24.8 4.1 21.78 882.5 11.63
## 58 11 2000 20.6 4.7 23.15 2704.2 13.24
## 59 12 2000 21.7 4.7 25.45 1699.2 13.29
## 60 13 2000 23.6 4.8 40.85 8642.8 16.03
## 61 14 2000 20.6 4.8 29.54 5817.0 15.58
## 62 15 2000 33.0 3.9 21.69 552.5 16.41
## 63 16 2000 26.8 3.4 19.93 605.8 19.55
## 64 17 2000 20.4 3.6 25.98 854.1 21.26
## 65 18 2000 21.1 3.1 20.94 777.6 19.76
## 66 19 2000 22.8 3.8 24.17 934.6 14.79
## 67 20 2000 26.4 3.1 23.13 664.5 7.88
## 68 21 2000 23.5 3.7 19.74 982.7 16.80
## 69 22 2000 19.9 3.8 22.91 1379.5 23.06
## 70 23 2000 20.9 4.0 26.23 2386.2 17.36
## 71 24 2000 20.6 3.9 21.73 918.7 16.00
## 72 25 2000 19.8 3.7 22.22 1041.7 14.75
## 73 26 2000 25.5 4.9 30.86 2289.4 13.69
## 74 27 2000 25.7 7.0 29.78 6701.6 11.64
## 75 28 2000 23.2 5.3 24.95 2014.3 10.27
## 76 29 2000 17.9 4.9 19.13 1696.3 13.20
## 77 30 2000 25.9 5.0 21.97 975.0 14.14
## 78 31 2000 22.6 3.6 22.69 672.5 19.26
## 79 32 2000 30.8 3.0 24.02 606.3 15.68
## 80 33 2000 19.5 4.3 24.98 882.5 8.13
## 81 34 2000 21.2 4.3 28.02 1276.8 11.39
## 82 35 2000 26.2 4.1 26.75 873.6 13.88
## 83 36 2000 19.6 4.9 24.40 806.8 13.37
## 84 37 2000 22.7 4.7 23.81 1031.9 8.57
## 85 38 2000 23.4 5.0 26.30 894.2 11.50
## 86 39 2000 25.6 5.3 29.85 696.8 25.00
## 87 40 2000 24.4 5.9 30.24 1832.4 13.44
## 88 41 2000 25.1 4.4 20.96 654.3 17.11
## 89 42 2000 24.6 4.9 25.30 937.8 15.61
## 90 43 2000 22.5 4.4 25.04 677.2 18.26
## 91 44 2000 26.6 4.5 26.42 690.3 14.58
## 92 45 2000 32.6 5.0 25.74 637.6 25.94
## 93 46 2000 26.9 4.9 30.12 550.9 26.67
## 94 47 2000 26.7 9.4 24.26 1137.3 26.13
## 95 1 2005 27.4 6.5 32.40 257.0 12.37
## 96 2 2005 36.8 8.4 25.40 448.4 16.27
## 97 3 2005 34.2 6.2 25.39 373.3 13.77
## 98 4 2005 26.9 6.9 28.98 754.0 10.29
## 99 5 2005 39.2 6.1 22.75 363.1 18.21
## 100 6 2005 31.1 4.8 21.81 426.7 11.96
## 101 7 2005 29.1 6.0 24.33 495.8 10.68
## 102 8 2005 23.7 5.9 23.13 748.3 11.47
## 103 9 2005 25.0 5.4 24.40 684.5 13.33
## 104 10 2005 25.3 5.7 23.59 882.1 11.14
## 105 11 2005 22.4 5.7 25.19 2749.5 11.91
## 106 12 2005 22.1 5.6 26.94 1736.2 13.15
## 107 13 2005 21.9 5.6 42.53 9009.5 14.82
## 108 14 2005 19.8 5.5 30.94 6021.9 14.11
## 109 15 2005 29.7 4.8 23.26 542.5 18.13
## 110 16 2005 30.7 4.4 21.82 600.8 27.77
## 111 17 2005 22.7 4.7 27.61 848.9 25.45
## 112 18 2005 23.5 4.2 22.30 770.3 27.31
## 113 19 2005 26.9 5.3 25.87 930.7 8.18
## 114 20 2005 25.4 4.6 24.16 659.6 8.68
## 115 21 2005 25.4 4.8 21.43 980.5 14.51
## 116 22 2005 21.9 4.6 24.65 1388.3 17.08
## 117 23 2005 20.7 4.6 28.75 2451.1 9.01
## 118 24 2005 20.0 4.7 24.02 923.3 9.28
## 119 25 2005 22.2 4.7 24.33 1070.8 14.24
## 120 26 2005 21.1 6.0 32.94 2291.9 9.55
## 121 27 2005 24.2 8.6 32.08 6703.4 9.09
## 122 28 2005 23.4 6.5 26.75 2026.7 6.87
## 123 29 2005 20.6 6.6 20.86 1671.1 9.11
## 124 30 2005 25.9 6.3 23.68 943.6 9.86
## 125 31 2005 24.4 5.5 25.32 665.5 20.03
## 126 32 2005 27.8 4.4 25.59 590.8 14.73
## 127 33 2005 21.6 5.3 27.74 885.2 7.33
## 128 34 2005 22.0 5.0 29.69 1275.2 13.23
## 129 35 2005 26.1 5.1 28.28 852.6 16.13
## 130 36 2005 20.0 7.3 26.91 792.7 9.99
## 131 37 2005 20.0 6.1 25.61 1020.8 7.72
## 132 38 2005 25.4 6.4 28.70 878.6 11.79
## 133 39 2005 29.8 7.9 31.76 681.5 17.46
## 134 40 2005 24.8 7.4 31.75 1841.5 10.20
## 135 41 2005 25.0 5.7 22.76 646.5 13.57
## 136 42 2005 29.3 6.5 27.11 913.1 13.73
## 137 43 2005 24.4 5.9 26.53 670.7 13.25
## 138 44 2005 24.3 6.1 28.50 683.2 14.19
## 139 45 2005 30.6 6.1 27.70 628.2 22.20
## 140 46 2005 26.2 6.9 31.61 540.5 19.88
## 141 47 2005 24.2 11.9 27.43 1171.4 19.48
## 142 1 2010 25.4 7.1 34.85 248.0 13.25
## 143 2 2010 29.5 9.0 27.58 424.7 15.70
## 144 3 2010 32.2 7.1 27.41 360.1 16.34
## 145 4 2010 22.9 7.8 31.25 746.6 14.44
## 146 5 2010 33.2 7.0 24.57 340.0 18.91
## 147 6 2010 26.4 5.8 23.17 409.4 14.19
## 148 7 2010 25.2 7.1 26.22 479.8 15.19
## 149 8 2010 24.0 6.7 25.75 745.8 15.31
## 150 9 2010 25.2 6.3 27.33 673.4 17.18
## 151 10 2010 25.9 6.3 26.21 872.7 14.91
## 152 11 2010 23.3 6.3 28.43 2795.0 13.07
## 153 12 2010 22.1 6.1 30.30 1760.1 15.25
## 154 13 2010 22.4 5.9 45.79 9460.6 16.80
## 155 14 2010 21.4 5.8 33.79 6167.2 18.56
## 156 15 2010 28.7 5.5 25.66 527.2 20.72
## 157 16 2010 23.1 5.2 24.17 590.1 27.87
## 158 17 2010 22.6 5.4 29.56 842.5 28.59
## 159 18 2010 20.2 5.2 24.50 750.7 27.17
## 160 19 2010 27.5 6.2 27.54 906.3 13.20
## 161 20 2010 23.6 5.4 25.71 649.6 10.58
## 162 21 2010 20.9 5.6 23.61 945.7 24.41
## 163 22 2010 23.2 5.8 26.76 1367.4 28.46
## 164 23 2010 20.0 5.1 31.52 2490.8 17.30
## 165 24 2010 19.4 5.1 26.89 907.4 16.24
## 166 25 2010 22.4 5.1 27.23 1088.1 18.58
## 167 26 2010 22.8 6.2 35.76 2239.1 20.61
## 168 27 2010 24.4 8.0 35.78 6728.7 15.68
## 169 28 2010 23.0 6.5 30.23 2013.6 16.33
## 170 29 2010 19.3 7.4 23.70 1645.1 15.88
## 171 30 2010 25.1 6.7 27.41 914.5 15.78
## 172 31 2010 24.9 5.9 27.00 646.4 18.31
## 173 32 2010 26.0 4.6 27.56 556.9 18.57
## 174 33 2010 21.0 7.2 30.02 873.4 12.16
## 175 34 2010 21.7 5.4 32.76 1249.0 15.86
## 176 35 2010 24.3 5.9 30.63 845.8 20.84
## 177 36 2010 19.6 7.6 29.02 767.0 15.06
## 178 37 2010 21.9 6.3 28.85 993.0 9.88
## 179 38 2010 21.1 7.3 30.96 858.7 14.41
## 180 39 2010 26.0 7.7 33.75 658.7 30.93
## 181 40 2010 23.5 7.8 34.95 1828.6 17.29
## 182 41 2010 26.1 6.3 24.74 637.5 19.41
## 183 42 2010 26.0 6.6 29.43 873.2 18.98
## 184 43 2010 25.1 6.7 28.75 665.2 20.73
## 185 44 2010 22.5 7.1 30.88 685.4 12.98
## 186 45 2010 27.2 7.0 29.75 615.1 28.11
## 187 46 2010 24.4 6.8 33.43 521.7 29.42
## 188 47 2010 25.6 11.0 29.39 1193.0 28.96
## 189 1 2015 19.5 4.6 37.29 240.5 12.75
## 190 2 2015 20.5 5.3 30.13 405.1 10.04
## 191 3 2015 23.3 4.0 30.36 344.5 10.94
## 192 4 2015 17.6 4.9 34.36 739.8 14.45
## 193 5 2015 25.8 4.3 27.92 319.3 14.91
## 194 6 2015 21.8 3.6 25.49 389.6 10.27
## 195 7 2015 21.6 4.4 30.59 453.9 12.84
## 196 8 2015 18.7 4.5 28.36 733.9 12.27
## 197 9 2015 19.7 4.3 28.84 661.9 16.51
## 198 10 2015 21.7 4.3 28.63 865.6 12.32
## 199 11 2015 18.1 4.3 30.48 2811.4 13.35
## 200 12 2015 19.5 4.1 32.37 1750.7 16.16
## 201 13 2015 17.7 3.9 47.30 9528.5 17.82
## 202 14 2015 17.0 3.9 35.50 6205.8 18.36
## 203 15 2015 22.0 3.7 27.60 508.1 14.68
## 204 16 2015 20.5 3.1 26.15 578.6 21.41
## 205 17 2015 18.4 3.4 31.51 829.1 21.65
## 206 18 2015 15.5 3.3 26.39 730.3 23.00
## 207 19 2015 16.8 4.4 29.53 874.8 11.15
## 208 20 2015 18.3 3.4 27.86 650.7 10.58
## 209 21 2015 18.9 3.4 25.80 918.9 22.67
## 210 22 2015 18.8 4.0 28.53 1345.8 28.05
## 211 23 2015 16.1 3.4 33.48 2504.6 18.03
## 212 24 2015 19.1 3.4 29.42 881.8 19.79
## 213 25 2015 17.5 3.5 28.45 1080.8 17.84
## 214 26 2015 16.7 4.4 38.21 2223.8 20.43
## 215 27 2015 19.1 5.3 37.53 6643.3 16.49
## 216 28 2015 17.8 4.6 32.70 1988.8 15.78
## 217 29 2015 15.9 4.9 25.70 1594.7 15.12
## 218 30 2015 19.2 4.5 29.35 864.1 15.38
## 219 31 2015 18.3 3.9 29.49 636.6 17.50
## 220 32 2015 23.0 2.9 30.21 534.6 17.06
## 221 33 2015 18.3 4.1 32.22 866.0 13.34
## 222 34 2015 17.6 3.7 34.49 1230.7 16.41
## 223 35 2015 20.0 4.0 33.32 823.0 20.61
## 224 36 2015 17.3 5.0 32.16 748.1 19.86
## 225 37 2015 16.3 4.0 31.55 970.9 12.10
## 226 38 2015 19.5 4.4 33.58 827.9 16.87
## 227 39 2015 15.8 4.9 36.43 626.1 29.67
## 228 40 2015 18.0 5.3 37.37 1847.4 18.68
## 229 41 2015 16.7 4.1 26.87 623.6 20.83
## 230 42 2015 17.0 4.4 31.94 821.6 23.92
## 231 43 2015 19.9 4.5 30.92 638.8 22.92
## 232 44 2015 16.6 4.5 33.20 648.4 16.78
## 233 45 2015 23.3 4.6 32.12 596.8 31.93
## 234 46 2015 19.1 4.7 35.66 497.5 36.64
## 235 47 2015 20.8 6.3 32.36 1226.2 14.25都道府県ID（pref）、データ年（year）、自殺率（suic）、失業率（unemp）、単独世帯割合（alone）、人口密度（popd）、降水量（rain）となっています。
人口密度の対数（lpopd）、因子化したデータ年（ydum）を作成します。
data1$lpopd <- log(data1$popd)
data1$ydum <- as.factor(data1$year)図９－１ 自殺率と完全失業率の散布図
ggplot(data1,aes(x=unemp,y=suic, shape=ydum))+geom_point()+xlab("失業率(%)")+ylab("自殺率（人口10万人あたり）")+theme_classic()+labs(shape="データ年")
固定効果モデル
fixed <- plm(suic~unemp+alone+lpopd+rain+ydum, data=data1,
index=c("pref", "year"), model="within")
summary(fixed)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = suic ~ unemp + alone + lpopd + rain + ydum, data = data1,
## model = "within", index = c("pref", "year"))
##
## Balanced Panel: n = 47, T = 5, N = 235
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.792045 -0.958101 -0.010295 0.780953 4.853801
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## unemp 1.214271 0.307632 3.9471 0.0001133 ***
## alone 0.229351 0.239283 0.9585 0.3391005
## lpopd 26.218551 4.575438 5.7303 4.147e-08 ***
## rain 0.119443 0.045588 2.6201 0.0095419 **
## ydum2000 5.531842 0.632418 8.7471 1.534e-15 ***
## ydum2005 4.608279 1.267395 3.6360 0.0003613 ***
## ydum2010 1.910615 1.912400 0.9991 0.3191035
## ydum2015 -0.276571 2.113398 -0.1309 0.8960277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2767
## Residual Sum of Squares: 455.07
## R-Squared: 0.83554
## Adj. R-Squared: 0.7862
## F-statistic: 114.309 on 8 and 180 DF, p-value: < 2.22e-16プールドモデル
pool <- plm(suic~unemp+alone+lpopd+rain+ydum, data=data1,
index=c("pref", "year"), model="pooling")
summary(pool)## Pooling Model
##
## Call:
## plm(formula = suic ~ unemp + alone + lpopd + rain + ydum, data = data1,
## model = "pooling", index = c("pref", "year"))
##
## Balanced Panel: n = 47, T = 5, N = 235
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -6.26245 -2.03015 -0.18391 1.43969 11.82567
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 31.734567 2.136790 14.8515 < 2.2e-16 ***
## unemp 0.419512 0.185211 2.2651 0.02446 *
## alone 0.068225 0.060181 1.1337 0.25814
## lpopd -2.609467 0.322157 -8.1000 3.417e-14 ***
## rain 0.070016 0.039770 1.7605 0.07967 .
## ydum2000 6.072405 0.635421 9.5565 < 2.2e-16 ***
## ydum2005 6.349399 0.729951 8.6984 7.041e-16 ***
## ydum2010 4.239894 0.817991 5.1833 4.830e-07 ***
## ydum2015 -0.110780 0.807738 -0.1371 0.89104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 5057.4
## Residual Sum of Squares: 2050.6
## R-Squared: 0.59454
## Adj. R-Squared: 0.58019
## F-statistic: 41.4238 on 8 and 226 DF, p-value: < 2.22e-16F検定
pFtest(fixed, pool)##
## F test for individual effects
##
## data: suic ~ unemp + alone + lpopd + rain + ydum
## F = 13.72, df1 = 46, df2 = 180, p-value < 2.2e-16
## alternative hypothesis: significant effects変量効果モデル
random <- plm(suic~unemp+alone+lpopd+rain+ydum, data=data1,
index=c("pref", "year"), model="random")
summary(random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = suic ~ unemp + alone + lpopd + rain + ydum, data = data1,
## model = "random", index = c("pref", "year"))
##
## Balanced Panel: n = 47, T = 5, N = 235
##
## Effects:
## var std.dev share
## idiosyncratic 2.528 1.590 0.277
## individual 6.593 2.568 0.723
## theta: 0.7331
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -4.922717 -1.015961 -0.091974 0.923814 6.563164
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 28.575689 4.150268 6.8853 5.768e-12 ***
## unemp 0.408450 0.234597 1.7411 0.0816712 .
## alone 0.050110 0.111294 0.4502 0.6525314
## lpopd -2.163947 0.657082 -3.2933 0.0009903 ***
## rain 0.105650 0.043569 2.4249 0.0153120 *
## ydum2000 6.113029 0.431316 14.1730 < 2.2e-16 ***
## ydum2005 6.499727 0.708589 9.1728 < 2.2e-16 ***
## ydum2010 4.284758 0.975539 4.3922 1.122e-05 ***
## ydum2015 -0.012794 1.034520 -0.0124 0.9901329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2930.1
## Residual Sum of Squares: 661.95
## R-Squared: 0.77409
## Adj. R-Squared: 0.76609
## Chisq: 774.394 on 8 DF, p-value: < 2.22e-16ラグランジュ乗数検定
plmtest(pool, type=c("bp"))##
## Lagrange Multiplier Test - (Breusch-Pagan)
##
## data: suic ~ unemp + alone + lpopd + rain + ydum
## chisq = 204.3, df = 1, p-value < 2.2e-16
## alternative hypothesis: significant effectsハウスマン検定
phtest(fixed,random)##
## Hausman Test
##
## data: suic ~ unemp + alone + lpopd + rain + ydum
## chisq = 40.511, df = 8, p-value = 2.572e-06
## alternative hypothesis: one model is inconsistent表９－２ プールド、固定効果、変量効果の各モデルの推定結果
stargazer(pool,fixed,random,type="text",star.cutoffs=c(0.1,0.05,0.01), keep.stat=c("n","rsq","adj.rsq"))##
## ==========================================
## Dependent variable:
## -----------------------------
## suic
## (1) (2) (3)
## ------------------------------------------
## unemp 0.420** 1.214*** 0.408*
## (0.185) (0.308) (0.235)
##
## alone 0.068 0.229 0.050
## (0.060) (0.239) (0.111)
##
## lpopd -2.609*** 26.219*** -2.164***
## (0.322) (4.575) (0.657)
##
## rain 0.070* 0.119*** 0.106**
## (0.040) (0.046) (0.044)
##
## ydum2000 6.072*** 5.532*** 6.113***
## (0.635) (0.632) (0.431)
##
## ydum2005 6.349*** 4.608*** 6.500***
## (0.730) (1.267) (0.709)
##
## ydum2010 4.240*** 1.911 4.285***
## (0.818) (1.912) (0.976)
##
## ydum2015 -0.111 -0.277 -0.013
## (0.808) (2.113) (1.035)
##
## Constant 31.735*** 28.576***
## (2.137) (4.150)
##
## ------------------------------------------
## Observations 235 235 235
## R2 0.595 0.836 0.774
## Adjusted R2 0.580 0.786 0.766
## ==========================================
## Note: *p<0.1; **p<0.05; ***p<0.01