Analysis
[1] "景気動向指数個別系列:遅行系列:法人税収入(億円):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 10062
2000 9565 10333 11141 10251 10238 10133 9884 10215 10078 10149 10062 10029
2001 9886 10194 9683 9410 11008 10264 10091 10080 9597 9664 9948 9336
2002 9438 9314 8840 9309 8247 8970 8838 9042 8703 8718 8862 8886
2003 8439 8697 9314 8879 8800 8746 8959 8600 9245 9303 8845 9138
2004 10315 8981 9342 9538 9579 9502 9633 9586 9733 10024 10000 10900
2005 9444 10897 10513 10369 10712 10953 10334 11208 11006 10763 11270 10717
2006 11354 12065 11407 12497 12508 12290 13859 12819 12979 13164 12869 12681
2007 14650 12645 13452 13085 13525 13609 13093 13363 13596 13199 13883 13869
2008 12272 12992 12312 12078 12549 12106 11657 12300 11915 11419 12414 11123
2009 9156 8970 9070 8876 6644 8221 8176 8089 7730 8033 7394 7987
2010 8803 7867 8600 8075 8032 8238 8740 8108 8138 9350 8191 8380
2011 7877 8713 7711 8576 8542 8830 8361 8367 8816 8239 8700 8715
2012 8062 8270 8850 8967 8610 9150 9301 9232 9404 9312 8962 9247
2013 9559 9458 9482 9378 8671 8332 8621 9347 8913 8737 8826 9009
2014 8913 9231 8979 8895 10549 9689 9514 9371 9550 9881 10405 9609
2015 9598 9426 9869 9496 9952 9737 10168 9901 10086 10067 10074 16068
2016 10264 10590 9959 10755 10024 10289 9817 10513 9858 9982 10108 10081
2017 10157 9768 10340 9970 9880 10547 10641 10527 11010 11135 10626 11329
2018 11175 11499 11200 11208 11595 11288 11564 11390 11508 11214 11696 11260
2019 12215 11060 11372 11096 11065 11656 11492 11677 11345
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-761.63 -191.08 -3.49 242.98 1025.93
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7937.816 124.252 63.885 < 0.0000000000000002 ***
ID 29.712 5.414 5.488 0.0000031 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 380.5 on 37 degrees of freedom
Multiple R-squared: 0.4487, Adjusted R-squared: 0.4338
F-statistic: 30.12 on 1 and 37 DF, p-value: 0.000003102
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15385, p-value = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.2181, p-value = 0.6974
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1244, df = 1, p-value = 0.289
Box-Ljung test
data: lm_residuals
X-squared = 0.52364, df = 1, p-value = 0.4693
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-848.6 -372.8 -96.6 162.8 5928.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8912.791 173.913 51.249 < 0.0000000000000002 ***
ID 34.077 3.685 9.248 0.0000000000000319 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 775.4 on 79 degrees of freedom
Multiple R-squared: 0.5198, Adjusted R-squared: 0.5138
F-statistic: 85.53 on 1 and 79 DF, p-value: 0.0000000000000319
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.24691, p-value = 0.01405
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8066, p-value = 0.1608
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.080485, df = 1, p-value = 0.7766
Box-Ljung test
data: lm_residuals
X-squared = 0.70574, df = 1, p-value = 0.4009
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-2814.7 -889.0 -320.2 821.1 2797.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9800.793 326.158 30.049 < 0.0000000000000002 ***
ID -26.318 9.455 -2.784 0.00728 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1237 on 57 degrees of freedom
Multiple R-squared: 0.1197, Adjusted R-squared: 0.1042
F-statistic: 7.748 on 1 and 57 DF, p-value: 0.007282
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.31454, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.313, df = 1, p-value = 0.000006577
Box-Ljung test
data: lm_residuals
X-squared = 38.712, df = 1, p-value = 0.0000000004912
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-840.7 -360.3 -100.8 148.5 5958.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8931.965 178.933 49.918 < 0.0000000000000002 ***
ID 35.675 3.936 9.065 0.0000000000001 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 782.6 on 76 degrees of freedom
Multiple R-squared: 0.5195, Adjusted R-squared: 0.5132
F-statistic: 82.17 on 1 and 76 DF, p-value: 0.0000000000001003
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.26923, p-value = 0.006781
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8428, p-value = 0.2073
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.10526, df = 1, p-value = 0.7456
Box-Ljung test
data: lm_residuals
X-squared = 0.45994, df = 1, p-value = 0.4977
