Analysis
[1] "株式平均利回り:株式平均利回り:第一部:有配会社平均利回り(%):株式会社日本取引所グループ"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 0.93 0.90
2000 0.89 0.89 0.88 0.90 0.93 0.88 0.98 1.00 1.02 1.10 1.09 1.14
2001 1.16 1.19 1.15 1.08 1.10 1.09 1.19 1.28 1.37 1.31 1.34 1.36
2002 1.44 1.37 1.35 1.32 1.26 1.37 1.37 1.41 1.43 1.51 1.49 1.56
2003 1.59 1.55 1.57 1.54 1.47 1.40 1.37 1.32 1.30 1.27 1.33 1.28
2004 1.27 1.23 1.12 1.10 1.14 1.08 1.21 1.22 1.25 1.28 1.27 1.21
2005 1.19 1.15 1.13 1.18 1.17 1.27 1.24 1.21 1.13 1.07 1.01 0.94
2006 0.90 0.96 0.92 0.94 1.03 1.19 1.23 1.18 1.21 1.22 1.24 1.19
2007 1.16 1.15 1.18 1.18 1.18 1.25 1.29 1.38 1.37 1.38 1.47 1.55
2008 1.69 1.72 1.84 1.71 1.63 1.80 1.82 1.89 2.16 2.58 2.57 2.48
2009 2.66 2.80 2.70 2.60 2.40 2.02 1.99 1.94 2.02 2.08 2.25 2.11
2010 2.12 2.11 1.92 1.87 1.97 2.01 2.03 2.15 2.08 2.19 2.07 1.96
2011 1.91 1.86 1.96 2.01 2.14 2.08 2.10 2.22 2.21 2.26 2.34 2.32
2012 2.23 2.08 2.02 2.12 2.42 2.26 2.34 2.35 2.32 2.32 2.23 2.07
2013 1.92 1.86 1.73 1.58 1.73 1.75 1.74 1.77 1.63 1.63 1.57 1.52
2014 1.58 1.63 1.62 1.68 1.79 1.68 1.64 1.63 1.59 1.60 1.53 1.51
2015 1.50 1.44 1.43 1.42 1.49 1.50 1.48 1.56 1.68 1.56 1.52 1.56
2016 1.68 1.83 1.74 1.77 1.86 2.01 1.92 1.98 1.93 1.83 1.77 1.71
2017 1.71 1.68 1.70 1.70 1.76 1.70 1.66 1.64 1.57 1.51 1.49 1.45
2018 1.44 1.51 1.54 1.50 1.66 1.68 1.68 1.71 1.64 1.83 1.79 2.05
2019 1.99 1.93 1.94 1.92 2.19 2.14 2.10 2.20 2.10 1.99
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.25392 -0.08806 0.02359 0.08826 0.25092
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.976113 0.038751 50.996 < 0.0000000000000002 ***
ID 0.007656 0.001689 4.534 0.0000589 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1187 on 37 degrees of freedom
Multiple R-squared: 0.3572, Adjusted R-squared: 0.3398
F-statistic: 20.56 on 1 and 37 DF, p-value: 0.0000589
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 = 0.7624, p-value = 0.000002439
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.41919, df = 1, p-value = 0.5173
Box-Ljung test
data: lm_residuals
X-squared = 15.948, df = 1, p-value = 0.00006512
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33238 -0.12935 -0.02215 0.12614 0.47613
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5909462 0.0401004 39.674 < 0.0000000000000002 ***
ID 0.0029264 0.0008293 3.529 0.000692 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.181 on 81 degrees of freedom
Multiple R-squared: 0.1332, Adjusted R-squared: 0.1225
F-statistic: 12.45 on 1 and 81 DF, p-value: 0.0006915
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13253, p-value = 0.4619
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.22492, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.5078, df = 1, p-value = 0.1133
Box-Ljung test
data: lm_residuals
X-squared = 60.742, df = 1, p-value = 0.00000000000000655
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.5107 -0.1646 -0.0350 0.1397 0.6575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1402864 0.0657170 32.568 <0.0000000000000002 ***
ID 0.0002051 0.0019050 0.108 0.915
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2492 on 57 degrees of freedom
Multiple R-squared: 0.0002034, Adjusted R-squared: -0.01734
F-statistic: 0.0116 on 1 and 57 DF, p-value: 0.9146
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.28655, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 15.441, df = 1, p-value = 0.0000851
Box-Ljung test
data: lm_residuals
X-squared = 41.568, df = 1, p-value = 0.0000000001139
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33830 -0.11986 -0.00566 0.11547 0.34830
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5445285 0.0378691 40.786 < 0.0000000000000002 ***
ID 0.0039623 0.0008123 4.878 0.00000555 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1678 on 78 degrees of freedom
Multiple R-squared: 0.2338, Adjusted R-squared: 0.2239
F-statistic: 23.79 on 1 and 78 DF, p-value: 0.00000555
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1125, p-value = 0.6953
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.25142, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.307, df = 1, p-value = 0.0007723
Box-Ljung test
data: lm_residuals
X-squared = 61.848, df = 1, p-value = 0.000000000000003664