Analysis
[1] "景気動向指数個別系列:一致系列:寄与度:投資財出荷指数(除輸送機械):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.21
2000 0.13 0.00 0.16 0.08 -0.14 0.33 -0.11 0.40 -0.31 0.18 -0.03 0.19
2001 -0.30 0.11 -0.33 -0.03 -0.24 -0.07 -0.32 -0.28 -0.15 -0.12 -0.02 0.07
2002 -0.17 -0.04 0.06 -0.28 0.59 -0.38 -0.05 0.29 0.06 -0.15 -0.04 -0.15
2003 0.03 0.16 -0.01 -0.46 0.25 0.05 -0.05 -0.01 0.33 0.24 -0.27 0.13
2004 0.35 -0.10 0.01 0.13 0.13 0.07 0.12 -0.12 0.02 -0.17 0.07 0.08
2005 0.03 -0.22 0.22 0.17 -0.23 0.10 -0.09 0.30 -0.17 0.06 0.24 0.11
2006 -0.25 -0.03 0.03 0.28 -0.08 0.27 -0.20 -0.11 0.06 0.18 -0.07 -0.02
2007 0.08 0.12 -0.47 0.26 0.15 -0.04 0.10 -0.06 -0.15 0.02 -0.10 0.09
2008 -0.19 -0.01 -0.05 -0.11 0.37 -0.56 -0.22 -0.20 -0.02 -0.11 -0.56 -0.40
2009 -0.37 -0.49 -0.08 -0.20 -0.06 -0.07 -0.13 0.34 0.11 0.00 0.03 0.17
2010 0.25 0.34 0.05 0.09 -0.29 0.32 0.08 0.01 0.17 0.14 0.05 -0.05
2011 0.01 0.36 -1.02 0.17 0.22 0.13 -0.05 0.06 -0.27 0.48 0.12 0.01
2012 -0.05 -0.21 0.09 -0.14 0.22 -0.31 -0.06 -0.19 -0.08 -0.18 -0.09 0.35
2013 -0.27 0.16 0.43 -0.21 0.05 -0.06 0.14 0.14 -0.02 0.19 0.06 0.07
2014 0.58 -0.41 0.49 -0.79 -0.08 0.08 0.11 -0.16 0.22 -0.04 -0.04 -0.07
2015 0.51 -0.46 0.10 0.02 -0.01 0.10 0.02 -0.20 0.09 -0.06 -0.17 -0.16
2016 0.24 -0.07 -0.01 0.06 -0.11 0.06 0.09 0.03 0.04 0.03 0.12 -0.08
2017 -0.07 -0.04 -0.18 0.37 0.15 0.05 -0.21 0.50 -0.34 0.13 0.17 0.18
2018 -0.34 -0.01 0.17 0.24 -0.28 -0.10 0.02 0.05 -0.05 0.40 -0.26 -0.05
2019 -0.52 0.32 -0.19 0.14 0.31 -0.43 0.12 0.09 0.67
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.05274 -0.08499 -0.00053 0.11673 0.47839
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.112794 0.082484 1.367 0.180
ID -0.004447 0.003594 -1.237 0.224
Residual standard error: 0.2526 on 37 degrees of freedom
Multiple R-squared: 0.03974, Adjusted R-squared: 0.01378
F-statistic: 1.531 on 1 and 37 DF, p-value: 0.2237
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20513, p-value = 0.3888
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.5194, p-value = 0.9321
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0060538, df = 1, p-value = 0.938
Box-Ljung test
data: lm_residuals
X-squared = 3.7337, df = 1, p-value = 0.05332
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.80719 -0.10300 0.00773 0.12426 0.64102
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0142932 0.0562158 0.254 0.800
ID 0.0001813 0.0011911 0.152 0.879
Residual standard error: 0.2506 on 79 degrees of freedom
Multiple R-squared: 0.0002934, Adjusted R-squared: -0.01236
F-statistic: 0.02318 on 1 and 79 DF, p-value: 0.8794
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 = 2.6917, p-value = 0.9991
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.025301, df = 1, p-value = 0.8736
Box-Ljung test
data: lm_residuals
X-squared = 13.139, df = 1, p-value = 0.0002891
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.01255 -0.13940 0.02396 0.16896 0.48814
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.121391 0.071172 -1.706 0.0935 .
ID 0.003255 0.002063 1.578 0.1201
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2699 on 57 degrees of freedom
Multiple R-squared: 0.04185, Adjusted R-squared: 0.02504
F-statistic: 2.49 on 1 and 57 DF, p-value: 0.1201
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.067797, p-value = 0.9994
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0099, p-value = 0.4607
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.051419, df = 1, p-value = 0.8206
Box-Ljung test
data: lm_residuals
X-squared = 0.15006, df = 1, p-value = 0.6985
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.79663 -0.09853 0.01042 0.10609 0.63435
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0008292 0.0567661 0.015 0.988
ID 0.0004464 0.0012485 0.358 0.722
Residual standard error: 0.2483 on 76 degrees of freedom
Multiple R-squared: 0.001679, Adjusted R-squared: -0.01146
F-statistic: 0.1278 on 1 and 76 DF, p-value: 0.7217
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.076923, p-value = 0.9766
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.709, p-value = 0.9991
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.10424, df = 1, p-value = 0.7468
Box-Ljung test
data: lm_residuals
X-squared = 13.11, df = 1, p-value = 0.0002937