Analysis
[1] "株式平均利回り:株式平均利回り:第一部:加重平均利回り(%):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 0.66 0.65
2000 0.64 0.62 0.63 0.64 0.66 0.67 0.67 0.69 0.69 0.69 0.72 0.75
2001 0.78 0.79 0.80 0.73 0.70 0.74 0.82 0.86 0.98 0.94 0.96 0.99
2002 1.00 1.03 0.93 0.94 0.92 0.96 0.94 0.98 1.03 1.08 1.10 1.10
2003 1.09 1.10 1.14 1.16 1.11 1.04 1.03 1.01 0.94 0.91 0.96 0.95
2004 0.90 0.91 0.84 0.80 0.86 0.83 0.97 1.00 0.99 1.01 1.01 1.00
2005 0.97 0.96 0.94 0.98 1.00 0.99 1.15 1.10 1.02 0.96 0.89 0.83
2006 0.81 0.81 0.80 0.77 0.82 0.91 1.16 1.12 1.12 1.10 1.13 1.09
2007 1.05 1.02 1.05 1.06 1.05 1.04 1.17 1.29 1.32 1.27 1.36 1.36
2008 1.54 1.54 1.66 1.59 1.50 1.50 1.79 1.86 1.99 2.56 2.68 2.76
2009 2.79 2.99 3.01 2.74 2.59 2.46 2.17 2.04 2.09 2.18 2.29 2.19
2010 2.09 2.18 2.07 1.93 1.84 1.83 1.89 1.93 1.93 1.96 1.91 1.81
2011 1.75 1.71 1.85 1.93 2.10 2.19 2.10 2.33 2.40 2.40 2.47 2.45
2012 2.41 2.26 2.12 2.21 2.47 2.54 2.47 2.46 2.48 2.50 2.45 2.28
2013 2.06 1.93 1.81 1.68 1.62 1.82 1.67 1.72 1.67 1.66 1.61 1.56
2014 1.53 1.63 1.65 1.68 1.87 1.84 1.80 1.80 1.75 1.83 1.66 1.63
2015 1.65 1.58 1.49 1.46 1.57 1.60 1.61 1.63 1.80 1.74 1.66 1.69
2016 1.86 1.99 1.94 1.98 2.06 2.19 2.19 2.17 2.13 2.09 2.02 1.88
2017 1.86 1.86 1.84 1.92 1.92 1.94 1.91 1.91 1.88 1.79 1.73 1.70
2018 1.63 1.75 1.81 1.79 1.89 1.98 2.02 2.03 2.00 2.05 2.11 2.27
2019 2.39 2.33 2.33 2.30 2.45 2.48 2.43 2.54 2.42 2.36
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.42032 -0.13666 0.02687 0.14954 0.36883
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.879339 0.063540 29.577 < 0.0000000000000002 ***
ID 0.013943 0.002769 5.036 0.0000126 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1946 on 37 degrees of freedom
Multiple R-squared: 0.4067, Adjusted R-squared: 0.3906
F-statistic: 25.36 on 1 and 37 DF, p-value: 0.00001262
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.28205, p-value = 0.08974
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.26846, p-value = 0.000000000000008286
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.7625, df = 1, p-value = 0.02909
Box-Ljung test
data: lm_residuals
X-squared = 30.524, df = 1, p-value = 0.00000003297
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.40633 -0.13026 -0.04037 0.15739 0.67198
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6009991 0.0447650 35.765 < 0.0000000000000002 ***
ID 0.0070215 0.0009258 7.584 0.0000000000492 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2021 on 81 degrees of freedom
Multiple R-squared: 0.4153, Adjusted R-squared: 0.408
F-statistic: 57.52 on 1 and 81 DF, p-value: 0.00000000004924
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.072289, p-value = 0.9829
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.18343, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0087354, df = 1, p-value = 0.9255
Box-Ljung test
data: lm_residuals
X-squared = 60.034, df = 1, p-value = 0.000000000000009326
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.63197 -0.27041 -0.02499 0.22310 0.85728
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.125050 0.092123 23.068 <0.0000000000000002 ***
ID 0.002306 0.002671 0.864 0.391
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3493 on 57 degrees of freedom
Multiple R-squared: 0.01292, Adjusted R-squared: -0.004402
F-statistic: 0.7458 on 1 and 57 DF, p-value: 0.3914
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15254, p-value = 0.5021
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.17088, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 15.023, df = 1, p-value = 0.0001062
Box-Ljung test
data: lm_residuals
X-squared = 48.701, df = 1, p-value = 0.000000000002981
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.4143 -0.1107 -0.0160 0.1346 0.3358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5477690 0.0400436 38.652 < 0.0000000000000002 ***
ID 0.0084162 0.0008589 9.799 0.00000000000000308 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1774 on 78 degrees of freedom
Multiple R-squared: 0.5518, Adjusted R-squared: 0.546
F-statistic: 96.01 on 1 and 78 DF, p-value: 0.000000000000003079
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1375, p-value = 0.4383
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.212, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.5914, df = 1, p-value = 0.005865
Box-Ljung test
data: lm_residuals
X-squared = 63.859, df = 1, p-value = 0.000000000000001332