Analysis
[1] "景気動向指数個別系列:遅行系列: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 0.10 0.11 0.09 0.11 0.10 0.11
2001 0.08 0.05 0.04 0.01 -0.02 -0.02 -0.06 -0.07 -0.11 -0.14 -0.16 -0.17
2002 -0.19 -0.18 -0.20 -0.14 -0.11 -0.13 -0.12 -0.10 -0.07 -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 0.15 0.13 0.17
2004 0.19 0.18 0.16 0.18 0.17 0.18 0.20 0.15 0.14 0.12 0.13 0.11
2005 0.12 0.10 0.09 0.10 0.08 0.07 0.07 0.06 0.08 0.06 0.07 0.06
2006 0.11 0.12 0.14 0.18 0.20 0.21 0.24 0.28 0.31 0.33 0.35 0.36
2007 0.36 0.36 0.34 0.33 0.29 0.31 0.29 0.28 0.25 0.27 0.25 0.27
2008 0.26 0.25 0.24 0.24 0.23 0.19 0.18 0.11 0.07 -0.02 -0.10 -0.22
2009 -0.37 -0.46 -0.45 -0.43 -0.38 -0.36 -0.36 -0.30 -0.26 -0.21 -0.20 -0.15
2010 -0.12 -0.09 -0.09 -0.10 -0.09 -0.09 -0.08 -0.08 -0.08 -0.09 -0.07 -0.09
2011 -0.10 -0.08 -0.20 -0.24 -0.20 -0.17 -0.15 -0.15 -0.14 -0.12 -0.16 -0.12
2012 -0.12 -0.12 -0.10 -0.13 -0.15 -0.19 -0.19 -0.20 -0.21 -0.23 -0.23 -0.21
2013 -0.20 -0.19 -0.15 -0.14 -0.13 -0.10 -0.08 -0.01 0.01 0.07 0.18 0.26
2014 0.43 0.52 0.58 0.47 0.43 0.38 0.37 0.32 0.30 0.25 0.20 0.17
2015 0.14 0.10 0.06 0.07 0.08 0.08 0.07 0.05 0.05 0.07 0.02 0.01
2016 0.02 -0.01 0.11 0.15 0.09 0.06 0.05 0.04 0.03 0.02 0.08 0.04
2017 0.03 0.03 0.02 0.07 0.08 0.13 0.13 0.15 0.17 0.17 0.21 0.20
2018 0.15 0.15 0.12 0.13 0.11 0.11 0.08 0.07 0.03 0.06 0.02 0.01
2019 -0.05 -0.01 -0.06 0.05 0.06 0.03 0.04 0.04 0.05
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.10910 -0.02629 0.01389 0.03705 0.06371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.098731 0.015169 -6.509 0.000000129 ***
ID -0.002166 0.000661 -3.277 0.00229 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04646 on 37 degrees of freedom
Multiple R-squared: 0.2249, Adjusted R-squared: 0.204
F-statistic: 10.74 on 1 and 37 DF, p-value: 0.002287
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 = 0.41546, p-value = 0.00000000004393
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.3077, df = 1, p-value = 0.06895
Box-Ljung test
data: lm_residuals
X-squared = 21.407, df = 1, p-value = 0.000003715
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33357 -0.06179 -0.01919 0.05513 0.45861
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1344414 0.0329931 4.075 0.000109 ***
ID -0.0008701 0.0006990 -1.245 0.216892
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1471 on 79 degrees of freedom
Multiple R-squared: 0.01924, Adjusted R-squared: 0.006822
F-statistic: 1.549 on 1 and 79 DF, p-value: 0.2169
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.19753, p-value = 0.08471
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.099216, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 25.585, df = 1, p-value = 0.0000004232
Box-Ljung test
data: lm_residuals
X-squared = 70.788, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33058 -0.04482 0.02223 0.05747 0.34893
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.117767 0.036364 -3.239 0.00201 **
ID -0.001165 0.001054 -1.105 0.27381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1379 on 57 degrees of freedom
Multiple R-squared: 0.02097, Adjusted R-squared: 0.003796
F-statistic: 1.221 on 1 and 57 DF, p-value: 0.2738
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25424, p-value = 0.04374
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.10408, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 25.538, df = 1, p-value = 0.0000004337
Box-Ljung test
data: lm_residuals
X-squared = 49.281, df = 1, p-value = 0.000000000002218
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.32003 -0.06737 -0.01234 0.05311 0.42012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1818648 0.0306613 5.931 0.0000000832 ***
ID -0.0018323 0.0006744 -2.717 0.00815 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1341 on 76 degrees of freedom
Multiple R-squared: 0.08854, Adjusted R-squared: 0.07655
F-statistic: 7.383 on 1 and 76 DF, p-value: 0.008152
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 = 0.316
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.12312, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 24.682, df = 1, p-value = 0.000000676
Box-Ljung test
data: lm_residuals
X-squared = 65.777, df = 1, p-value = 0.0000000000000005551