Analysis
[1] "景気動向指数個別系列:遅行系列:寄与度:第3次産業活動指数(対事業所サービス業):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.13
2000 -0.04 -0.26 0.33 -0.10 -0.05 0.02 0.03 0.10 0.03 0.06 -0.05 -0.01
2001 -0.03 0.09 -0.05 0.22 -0.18 -0.13 -0.04 0.03 -0.03 -0.01 0.10 -0.03
2002 -0.06 -0.05 0.07 -0.06 0.01 -0.01 0.03 -0.04 -0.07 -0.07 -0.02 0.06
2003 0.13 0.04 0.34 -0.37 0.05 0.00 0.00 0.00 0.08 0.02 -0.14 -0.04
2004 0.13 -0.20 -0.14 0.17 0.03 0.00 0.08 -0.03 -0.04 0.05 0.18 -0.05
2005 0.11 -0.09 0.15 -0.04 -0.28 0.28 -0.19 0.21 0.02 -0.24 0.24 -0.03
2006 0.12 -0.04 -0.05 -0.04 0.14 -0.01 -0.07 0.08 -0.03 -0.01 -0.08 0.07
2007 -0.07 0.02 -0.03 0.10 -0.04 -0.01 0.12 -0.29 -0.10 0.06 0.14 -0.10
2008 -0.09 -0.08 -0.05 -0.04 -0.05 -0.21 -0.19 -0.08 -0.10 0.04 -0.14 0.02
2009 -0.20 -0.19 0.07 -0.27 -0.14 0.08 -0.04 0.31 0.05 -0.31 -0.05 -0.26
2010 0.24 -0.08 -0.03 -0.18 0.14 0.00 0.23 -0.29 -0.02 0.06 0.12 -0.14
2011 0.19 0.11 -0.23 0.16 -0.06 0.14 -0.03 0.06 0.20 -0.08 -0.02 0.17
2012 -0.16 0.18 0.22 -0.04 0.07 -0.17 -0.04 0.05 0.00 0.07 0.04 0.15
2013 -0.29 -0.02 0.31 0.04 0.35 -0.24 0.23 0.15 -0.32 0.07 -0.16 -0.06
2014 0.05 0.01 0.05 0.07 -0.09 0.01 0.12 -0.32 0.06 0.00 -0.02 0.13
2015 0.01 0.06 -0.06 0.10 -0.12 0.04 -0.08 0.12 -0.07 -0.13 0.13 -0.12
2016 0.26 -0.10 0.11 0.23 -0.23 0.04 0.04 -0.07 0.16 -0.19 0.08 0.07
2017 0.12 -0.01 0.06 0.05 -0.17 0.10 -0.11 -0.01 -0.15 0.23 -0.10 0.11
2018 -0.16 0.08 0.12 0.00 -0.06 0.08 -0.05 -0.05 0.09 0.04 0.09 0.05
2019 -0.01 -0.06 0.15 0.09 -0.17 -0.13 -0.02 -0.15 0.11
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.27082 -0.09176 -0.00112 0.12774 0.28228
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.055479 0.048162 -1.152 0.257
ID 0.003300 0.002099 1.572 0.124
Residual standard error: 0.1475 on 37 degrees of freedom
Multiple R-squared: 0.06263, Adjusted R-squared: 0.03729
F-statistic: 2.472 on 1 and 37 DF, p-value: 0.1244
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 = 2.6549, p-value = 0.9743
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 5.1508, df = 1, p-value = 0.02324
Box-Ljung test
data: lm_residuals
X-squared = 5.8349, df = 1, p-value = 0.01571
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.32991 -0.08793 0.02965 0.08610 0.33974
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01071296 0.03065365 0.349 0.728
ID -0.00008966 0.00064947 -0.138 0.891
Residual standard error: 0.1367 on 79 degrees of freedom
Multiple R-squared: 0.0002412, Adjusted R-squared: -0.01241
F-statistic: 0.01906 on 1 and 79 DF, p-value: 0.8906
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14815, p-value = 0.338
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.6918, p-value = 0.9991
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 8.2738, df = 1, p-value = 0.004022
Box-Ljung test
data: lm_residuals
X-squared = 12.159, df = 1, p-value = 0.0004884
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35328 -0.09452 -0.00015 0.12218 0.35903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.092858 0.039971 -2.323 0.0238 *
ID 0.002739 0.001159 2.364 0.0215 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1516 on 57 degrees of freedom
Multiple R-squared: 0.0893, Adjusted R-squared: 0.07332
F-statistic: 5.589 on 1 and 57 DF, p-value: 0.0215
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11864, p-value = 0.8052
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.4904, p-value = 0.9629
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.20046, df = 1, p-value = 0.6544
Box-Ljung test
data: lm_residuals
X-squared = 4.4578, df = 1, p-value = 0.03474
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33190 -0.08632 0.02919 0.08712 0.33755
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0127273 0.0298399 0.427 0.671
ID -0.0001372 0.0006563 -0.209 0.835
Residual standard error: 0.1305 on 76 degrees of freedom
Multiple R-squared: 0.0005747, Adjusted R-squared: -0.01258
F-statistic: 0.0437 on 1 and 76 DF, p-value: 0.835
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.21795, p-value = 0.04892
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.8717, p-value = 1
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 5.0772, df = 1, p-value = 0.02424
Box-Ljung test
data: lm_residuals
X-squared = 15.736, df = 1, p-value = 0.00007284