Analysis
[1] "景気動向指数個別系列:先行系列:長期国債(10年)新発債流通利回り(%):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 1.65
2000 1.71 1.84 1.77 1.76 1.66 1.76 1.68 1.90 1.84 1.82 1.62 1.64
2001 1.50 1.30 1.27 1.29 1.24 1.21 1.33 1.38 1.42 1.30 1.36 1.37
2002 1.48 1.53 1.40 1.37 1.39 1.32 1.32 1.18 1.18 0.99 1.00 0.90
2003 0.81 0.78 0.70 0.61 0.53 0.82 0.93 1.47 1.38 1.47 1.31 1.36
2004 1.32 1.22 1.44 1.54 1.53 1.78 1.85 1.54 1.44 1.49 1.45 1.44
2005 1.32 1.47 1.32 1.24 1.25 1.17 1.31 1.34 1.48 1.55 1.45 1.47
2006 1.56 1.59 1.77 1.92 1.83 1.92 1.92 1.62 1.67 1.72 1.65 1.68
2007 1.70 1.63 1.65 1.62 1.75 1.87 1.79 1.60 1.68 1.60 1.46 1.50
2008 1.44 1.36 1.28 1.58 1.74 1.61 1.53 1.41 1.48 1.48 1.40 1.17
2009 1.27 1.27 1.34 1.43 1.48 1.35 1.42 1.31 1.30 1.41 1.26 1.29
2010 1.32 1.30 1.40 1.28 1.26 1.09 1.06 0.98 0.93 0.92 1.19 1.11
2011 1.22 1.26 1.26 1.20 1.15 1.13 1.08 1.03 1.02 1.05 1.07 0.98
2012 0.97 0.96 0.99 0.89 0.83 0.83 0.78 0.80 0.77 0.78 0.70 0.80
2013 0.74 0.67 0.56 0.60 0.86 0.86 0.80 0.72 0.68 0.59 0.60 0.74
2014 0.62 0.58 0.64 0.62 0.57 0.57 0.53 0.49 0.53 0.45 0.42 0.33
2015 0.28 0.33 0.40 0.34 0.39 0.46 0.41 0.38 0.35 0.30 0.30 0.27
2016 0.10 -0.07 -0.05 -0.09 -0.12 -0.23 -0.20 -0.07 -0.09 -0.05 0.02 0.04
2017 0.09 0.05 0.07 0.02 0.04 0.08 0.08 0.01 0.06 0.07 0.04 0.05
2018 0.08 0.05 0.05 0.05 0.03 0.03 0.06 0.11 0.13 0.13 0.09 -0.01
2019 0.00 -0.02 -0.10 -0.05 -0.10 -0.17 -0.16 -0.28 -0.22
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.24622 -0.04956 0.02122 0.06827 0.17075
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.350175 0.033127 40.76 < 0.0000000000000002 ***
ID -0.014496 0.001443 -10.04 0.00000000000409 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1015 on 37 degrees of freedom
Multiple R-squared: 0.7316, Adjusted R-squared: 0.7243
F-statistic: 100.9 on 1 and 37 DF, p-value: 0.000000000004088
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 = 0.59762, p-value = 0.00000003614
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.8814, df = 1, p-value = 0.08961
Box-Ljung test
data: lm_residuals
X-squared = 20.245, df = 1, p-value = 0.000006812
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.44759 -0.05113 0.02712 0.07831 0.24008
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6869784 0.0328625 20.91 <0.0000000000000002 ***
ID -0.0111759 0.0006963 -16.05 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1465 on 79 degrees of freedom
Multiple R-squared: 0.7653, Adjusted R-squared: 0.7624
F-statistic: 257.6 on 1 and 79 DF, p-value: < 0.00000000000000022
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 = 0.20835, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0021399, df = 1, p-value = 0.9631
Box-Ljung test
data: lm_residuals
X-squared = 67.257, df = 1, p-value = 0.000000000000000222
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.27651 -0.05585 0.01022 0.06747 0.19624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5576563 0.0278894 55.85 <0.0000000000000002 ***
ID -0.0138936 0.0008085 -17.18 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1058 on 57 degrees of freedom
Multiple R-squared: 0.8382, Adjusted R-squared: 0.8354
F-statistic: 295.3 on 1 and 57 DF, p-value: < 0.00000000000000022
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 = 0.64287, p-value = 0.0000000002498
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.6368, df = 1, p-value = 0.2008
Box-Ljung test
data: lm_residuals
X-squared = 24.125, df = 1, p-value = 0.0000009029
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.44791 -0.05075 0.02889 0.08021 0.23902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6545654 0.0340251 19.24 <0.0000000000000002 ***
ID -0.0111964 0.0007484 -14.96 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1488 on 76 degrees of freedom
Multiple R-squared: 0.7465, Adjusted R-squared: 0.7432
F-statistic: 223.8 on 1 and 76 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12821, p-value = 0.546
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.20054, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.090455, df = 1, p-value = 0.7636
Box-Ljung test
data: lm_residuals
X-squared = 65.521, df = 1, p-value = 0.0000000000000005551