Analysis
[1] "景気動向指数個別系列:先行系列:寄与度:一致指数トレンド成分:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 0.02
2000 0.06 0.05 0.05 0.06 0.07 0.09 0.11 0.11 0.10 0.11 0.11 0.11
2001 0.08 0.05 0.04 0.01 -0.02 -0.02 -0.05 -0.07 -0.11 -0.13 -0.15 -0.16
2002 -0.18 -0.17 -0.19 -0.14 -0.11 -0.13 -0.12 -0.10 -0.08 -0.06 -0.02 -0.03
2003 -0.02 0.02 0.06 0.04 0.07 0.08 0.07 0.10 0.11 0.16 0.14 0.17
2004 0.19 0.18 0.16 0.18 0.17 0.19 0.20 0.15 0.14 0.13 0.14 0.12
2005 0.13 0.10 0.09 0.11 0.08 0.07 0.07 0.06 0.08 0.06 0.07 0.07
2006 0.11 0.12 0.15 0.19 0.20 0.21 0.24 0.28 0.31 0.33 0.35 0.36
2007 0.35 0.35 0.34 0.33 0.29 0.30 0.28 0.27 0.24 0.26 0.23 0.25
2008 0.24 0.23 0.22 0.22 0.21 0.17 0.17 0.10 0.06 -0.02 -0.08 -0.18
2009 -0.29 -0.35 -0.36 -0.36 -0.33 -0.33 -0.35 -0.30 -0.27 -0.23 -0.21 -0.16
2010 -0.13 -0.10 -0.09 -0.11 -0.11 -0.10 -0.09 -0.09 -0.08 -0.10 -0.08 -0.10
2011 -0.11 -0.09 -0.22 -0.26 -0.21 -0.18 -0.17 -0.16 -0.15 -0.13 -0.17 -0.13
2012 -0.13 -0.13 -0.10 -0.14 -0.16 -0.20 -0.19 -0.21 -0.21 -0.23 -0.23 -0.21
2013 -0.21 -0.21 -0.17 -0.16 -0.15 -0.11 -0.09 -0.02 0.01 0.07 0.20 0.29
2014 0.47 0.57 0.61 0.49 0.43 0.38 0.37 0.32 0.30 0.25 0.20 0.17
2015 0.14 0.09 0.06 0.07 0.08 0.08 0.08 0.05 0.05 0.07 0.02 0.01
2016 0.02 -0.01 0.11 0.14 0.09 0.05 0.05 0.03 0.03 0.02 0.08 0.04
2017 0.03 0.03 0.02 0.07 0.07 0.13 0.13 0.15 0.16 0.17 0.20 0.20
2018 0.14 0.14 0.12 0.13 0.11 0.10 0.08 0.06 0.03 0.06 0.01 0.01
2019 -0.04 -0.01 -0.05 0.04 0.05 0.02 0.03 0.04 0.05
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.11587 -0.02710 0.01475 0.03595 0.07084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1121727 0.0156450 -7.170 0.0000000169 ***
ID -0.0019555 0.0006817 -2.868 0.00678 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04791 on 37 degrees of freedom
Multiple R-squared: 0.1819, Adjusted R-squared: 0.1598
F-statistic: 8.228 on 1 and 37 DF, p-value: 0.006776
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 = 0.45939, p-value = 0.0000000002934
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.6351, df = 1, p-value = 0.05657
Box-Ljung test
data: lm_residuals
X-squared = 19.949, df = 1, p-value = 0.000007953
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.34578 -0.06047 -0.02218 0.05593 0.48743
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1367284 0.0346948 3.941 0.000174 ***
ID -0.0009440 0.0007351 -1.284 0.202829
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1547 on 79 degrees of freedom
Multiple R-squared: 0.02045, Adjusted R-squared: 0.008049
F-statistic: 1.649 on 1 and 79 DF, p-value: 0.2028
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1358, p-value = 0.4462
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.094466, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 25.508, df = 1, p-value = 0.0000004406
Box-Ljung test
data: lm_residuals
X-squared = 71.301, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.24126 -0.03988 0.01033 0.05055 0.31095
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0991701 0.0314411 -3.154 0.00257 **
ID -0.0017791 0.0009114 -1.952 0.05586 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1192 on 57 degrees of freedom
Multiple R-squared: 0.06266, Adjusted R-squared: 0.04621
F-statistic: 3.81 on 1 and 57 DF, p-value: 0.05586
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23729, p-value = 0.07193
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.11039, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 26.961, df = 1, p-value = 0.0000002077
Box-Ljung test
data: lm_residuals
X-squared = 48.536, df = 1, p-value = 0.000000000003243
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.34496 -0.07146 -0.01919 0.05199 0.44664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1869264 0.0321825 5.808 0.000000139 ***
ID -0.0019637 0.0007078 -2.774 0.00696 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1407 on 76 degrees of freedom
Multiple R-squared: 0.09196, Adjusted R-squared: 0.08001
F-statistic: 7.697 on 1 and 76 DF, p-value: 0.006958
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.1624
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.11784, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 24.396, df = 1, p-value = 0.0000007844
Box-Ljung test
data: lm_residuals
X-squared = 65.857, df = 1, p-value = 0.0000000000000004441