Analysis
[1] "景気動向指数個別系列:一致系列:寄与度:生産指数(鉱工業):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.02
2000 0.00 -0.04 0.21 0.16 -0.05 0.18 -0.07 0.17 -0.26 0.20 0.04 0.16
2001 -0.56 0.14 -0.21 -0.13 -0.25 -0.14 -0.19 -0.14 -0.24 -0.01 -0.19 0.12
2002 -0.08 0.18 0.10 -0.07 0.50 -0.13 0.09 0.04 0.09 0.01 -0.05 -0.01
2003 0.06 -0.05 0.07 -0.16 0.17 -0.10 0.09 -0.16 0.36 0.22 -0.04 -0.01
2004 0.21 -0.03 -0.06 0.22 0.00 0.03 0.16 -0.15 0.02 -0.20 0.13 -0.18
2005 0.30 -0.03 0.05 0.06 -0.09 0.04 -0.11 0.01 0.13 -0.06 0.21 0.03
2006 0.06 -0.01 0.07 0.27 -0.21 0.17 0.04 0.04 -0.01 0.10 0.04 0.03
2007 -0.17 0.08 -0.01 -0.07 0.16 0.01 0.01 0.23 -0.24 0.26 -0.21 0.09
2008 0.06 0.03 -0.14 -0.09 0.06 -0.39 -0.03 -0.47 0.15 -0.31 -0.79 -0.79
2009 -0.88 -0.78 0.13 0.28 0.35 0.19 0.14 0.15 0.35 0.28 0.23 0.09
2010 0.29 0.05 0.03 0.13 -0.02 -0.09 0.13 0.05 0.19 -0.31 0.19 0.07
2011 0.06 0.07 -0.96 0.07 0.49 0.43 0.15 0.21 -0.11 0.22 -0.27 0.25
2012 0.05 -0.02 -0.02 -0.06 -0.23 -0.10 -0.06 -0.18 -0.27 0.04 -0.11 0.17
2013 -0.20 0.23 0.16 0.00 0.21 -0.14 0.22 0.03 0.13 0.03 0.08 -0.01
2014 0.26 -0.16 0.19 -0.64 0.25 -0.22 -0.03 -0.09 0.16 -0.04 0.00 -0.07
2015 0.36 -0.33 -0.07 0.03 0.00 0.12 -0.02 -0.23 0.27 0.01 -0.11 -0.19
2016 0.21 -0.12 0.06 -0.06 -0.11 0.09 0.08 0.09 0.03 0.04 0.13 0.00
2017 -0.15 0.10 -0.01 0.35 -0.24 0.13 -0.11 0.20 -0.14 0.04 0.12 0.21
2018 -0.60 0.28 0.14 -0.08 0.04 -0.15 0.01 -0.03 -0.01 0.28 -0.13 0.01
2019 -0.34 0.09 -0.08 0.08 0.28 -0.46 0.19 -0.18 0.25
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.99988 -0.06802 0.00922 0.12287 0.46103
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.138003 0.077957 1.770 0.0849 .
ID -0.005451 0.003397 -1.605 0.1170
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2388 on 37 degrees of freedom
Multiple R-squared: 0.06508, Adjusted R-squared: 0.03981
F-statistic: 2.575 on 1 and 37 DF, p-value: 0.117
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 = 0.9885
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.9749, p-value = 0.4008
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0032552, df = 1, p-value = 0.9545
Box-Ljung test
data: lm_residuals
X-squared = 0.0019474, df = 1, p-value = 0.9648
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.65790 -0.11384 0.00994 0.12580 0.34534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0236667 0.0436464 0.542 0.589
ID -0.0003604 0.0009247 -0.390 0.698
Residual standard error: 0.1946 on 79 degrees of freedom
Multiple R-squared: 0.001919, Adjusted R-squared: -0.01071
F-statistic: 0.1519 on 1 and 79 DF, p-value: 0.6978
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.18519, p-value = 0.1245
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.9799, p-value = 1
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.19956, df = 1, p-value = 0.6551
Box-Ljung test
data: lm_residuals
X-squared = 21.79, df = 1, p-value = 0.000003042
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.95427 -0.15750 0.07906 0.21067 0.48908
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.122209 0.083643 -1.461 0.149
ID 0.003328 0.002425 1.372 0.175
Residual standard error: 0.3172 on 57 degrees of freedom
Multiple R-squared: 0.03199, Adjusted R-squared: 0.01501
F-statistic: 1.884 on 1 and 57 DF, p-value: 0.1753
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.30508, p-value = 0.00792
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1844, p-value = 0.0002969
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.8126, df = 1, p-value = 0.02825
Box-Ljung test
data: lm_residuals
X-squared = 10.147, df = 1, p-value = 0.001446
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.65330 -0.11087 0.00983 0.12255 0.34891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0164902 0.0445036 0.371 0.712
ID -0.0002455 0.0009788 -0.251 0.803
Residual standard error: 0.1946 on 76 degrees of freedom
Multiple R-squared: 0.0008267, Adjusted R-squared: -0.01232
F-statistic: 0.06288 on 1 and 76 DF, p-value: 0.8027
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 = 3.0213, p-value = 1
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.21986, df = 1, p-value = 0.6391
Box-Ljung test
data: lm_residuals
X-squared = 22.063, df = 1, p-value = 0.000002639