Analysis
[1] "景気動向指数個別系列:遅行系列:寄与度:最終需要財在庫指数:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.09
2000 0.10 -0.05 0.15 -0.02 -0.04 0.01 -0.05 0.04 -0.16 0.14 0.01 -0.05
2001 0.00 0.02 0.00 0.15 0.12 0.03 -0.07 0.11 -0.05 -0.07 -0.07 0.00
2002 -0.01 -0.05 -0.14 -0.10 0.03 -0.20 0.07 -0.14 0.07 0.23 -0.24 0.20
2003 0.29 -0.26 -0.01 -0.05 0.06 -0.10 0.38 -0.26 0.10 0.05 -0.01 -0.15
2004 0.01 0.04 0.18 0.36 -0.12 0.06 -0.11 0.16 0.34 -0.38 0.05 -0.28
2005 0.31 0.20 0.16 -0.17 0.01 0.16 0.11 0.02 0.12 -0.38 0.18 0.13
2006 0.06 0.07 0.02 0.25 -0.23 0.12 -0.08 -0.04 -0.04 0.24 0.06 0.22
2007 -0.19 -0.10 -0.11 0.04 0.14 -0.11 0.09 0.10 0.07 0.20 0.14 -0.08
2008 -0.14 0.13 -0.12 -0.46 0.16 0.05 0.21 -0.31 0.17 0.06 -0.15 -0.12
2009 0.18 -0.65 -0.30 -0.26 -0.20 -0.14 0.03 -0.14 0.00 -0.19 0.05 -0.11
2010 0.14 0.04 -0.12 0.15 0.01 -0.01 -0.10 -0.09 0.10 -0.12 0.21 0.24
2011 0.36 -0.21 -0.55 0.28 0.45 -0.03 0.02 0.37 -0.03 0.08 -0.04 -0.18
2012 0.28 0.22 0.46 0.32 -0.23 0.01 0.19 0.11 0.04 0.08 0.05 -0.17
2013 -0.34 -0.18 -0.15 -0.09 -0.06 -0.32 0.29 -0.13 0.18 0.11 -0.21 0.15
2014 0.30 -0.07 -0.37 0.23 0.46 0.35 0.02 0.09 -0.01 -0.09 0.02 -0.02
2015 -0.07 -0.14 -0.35 -0.06 -0.21 0.01 0.04 -0.12 -0.04 -0.01 0.17 -0.04
2016 -0.12 -0.16 0.35 -0.15 0.04 0.09 -0.12 0.17 0.05 -0.28 -0.01 0.06
2017 0.01 0.08 0.22 0.12 -0.07 -0.16 -0.21 0.25 0.08 0.24 0.06 0.04
2018 -0.23 0.06 0.27 -0.28 0.06 -0.26 -0.05 0.00 -0.26 0.10 0.06 0.18
2019 0.04 -0.05 0.13 -0.11 0.13 -0.14 0.00 0.02 -0.12
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.59748 -0.11531 -0.02306 0.13752 0.39667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.005169 0.068280 -0.076 0.940
ID 0.002925 0.002975 0.983 0.332
Residual standard error: 0.2091 on 37 degrees of freedom
Multiple R-squared: 0.02546, Adjusted R-squared: -0.0008806
F-statistic: 0.9666 on 1 and 37 DF, p-value: 0.3319
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 = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8283, p-value = 0.2374
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.29506, df = 1, p-value = 0.587
Box-Ljung test
data: lm_residuals
X-squared = 0.10906, df = 1, p-value = 0.7412
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35322 -0.11723 -0.00070 0.09615 0.47599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0226821 0.0394460 -0.575 0.567
ID 0.0003936 0.0008358 0.471 0.639
Residual standard error: 0.1759 on 79 degrees of freedom
Multiple R-squared: 0.0028, Adjusted R-squared: -0.009823
F-statistic: 0.2218 on 1 and 79 DF, p-value: 0.6389
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 = 1.9043, p-value = 0.2919
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.7339, df = 1, p-value = 0.02957
Box-Ljung test
data: lm_residuals
X-squared = 0.047573, df = 1, p-value = 0.8273
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.61033 -0.11611 0.00041 0.15988 0.43611
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.059509 0.058500 -1.017 0.313
ID 0.001984 0.001696 1.170 0.247
Residual standard error: 0.2218 on 57 degrees of freedom
Multiple R-squared: 0.02344, Adjusted R-squared: 0.006309
F-statistic: 1.368 on 1 and 57 DF, p-value: 0.247
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15254, p-value = 0.5021
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.6523, p-value = 0.06807
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.075547, df = 1, p-value = 0.7834
Box-Ljung test
data: lm_residuals
X-squared = 1.5439, df = 1, p-value = 0.214
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.37812 -0.11949 0.00597 0.09757 0.45234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0108858 0.0396108 0.275 0.784
ID -0.0002301 0.0008712 -0.264 0.792
Residual standard error: 0.1732 on 76 degrees of freedom
Multiple R-squared: 0.0009174, Adjusted R-squared: -0.01223
F-statistic: 0.06979 on 1 and 76 DF, p-value: 0.7924
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10256, p-value = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0268, p-value = 0.5006
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.2806, df = 1, p-value = 0.03855
Box-Ljung test
data: lm_residuals
X-squared = 0.027523, df = 1, p-value = 0.8682