Analysis
[1] "株式平均利回り:株式平均利回り:第二部:単純平均利回り(%):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 0.83 0.86
2000 0.95 0.85 0.89 0.99 1.00 1.01 1.04 1.23 1.25 1.34 1.39 1.45
2001 1.50 1.44 1.54 1.51 1.43 1.47 1.58 1.63 1.80 1.77 1.80 1.89
2002 1.95 2.00 1.94 1.94 1.87 1.86 1.78 1.84 1.95 2.06 2.19 2.26
2003 2.19 2.13 2.20 2.15 2.06 1.94 1.84 1.79 1.70 1.63 1.69 1.74
2004 1.61 1.55 1.42 1.29 1.34 1.31 1.55 1.53 1.56 1.63 1.65 1.68
2005 1.57 1.48 1.32 1.33 1.33 1.33 1.37 1.35 1.30 1.27 1.20 1.11
2006 1.04 1.02 1.07 1.05 1.12 1.23 1.36 1.36 1.36 1.39 1.43 1.41
2007 1.40 1.36 1.37 1.41 1.41 1.38 1.44 1.56 1.64 1.63 1.73 1.78
2008 1.97 1.97 2.05 2.06 1.95 1.94 2.15 2.27 2.43 2.90 2.95 3.02
2009 3.01 3.14 3.22 3.13 3.01 2.75 2.43 2.35 2.32 2.42 2.56 2.58
2010 2.47 2.47 2.39 2.24 2.23 2.23 2.25 2.29 2.30 2.36 2.37 2.26
2011 2.14 2.05 2.13 2.15 2.22 2.25 2.24 2.40 2.43 2.47 2.52 2.45
2012 2.40 2.28 2.16 2.20 2.29 2.36 2.32 2.34 2.38 2.41 2.38 2.26
2013 2.08 2.01 1.88 1.82 1.77 1.95 1.92 1.94 1.89 1.84 1.82 1.83
2014 1.71 1.75 1.76 1.79 1.90 1.87 1.77 1.78 1.72 1.77 1.72 1.67
2015 1.66 1.65 1.60 1.57 1.61 1.60 1.59 1.66 1.76 1.73 1.72 1.73
2016 1.80 1.89 1.87 1.91 1.98 2.08 2.08 2.02 2.02 1.96 1.91 1.81
2017 1.77 1.71 1.65 1.72 1.74 1.74 1.68 1.64 1.66 1.57 1.53 1.49
2018 1.40 1.45 1.47 1.49 1.52 1.60 1.66 1.69 1.69 1.74 1.83 1.96
2019 1.99 1.93 1.92 1.92 2.06 2.16 2.12 2.19 2.17 2.11
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.27804 -0.09216 0.01020 0.08108 0.23694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.347355 0.040536 57.908 <0.0000000000000002 ***
ID -0.001073 0.001766 -0.607 0.547
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1241 on 37 degrees of freedom
Multiple R-squared: 0.009873, Adjusted R-squared: -0.01689
F-statistic: 0.3689 on 1 and 37 DF, p-value: 0.5473
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 = 0.9114
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.37031, p-value = 0.000000000004823
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.4859, df = 1, p-value = 0.2229
Box-Ljung test
data: lm_residuals
X-squared = 27.565, df = 1, p-value = 0.0000001519
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.3975 -0.1344 -0.0324 0.1244 0.4548
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.8053071 0.0417864 43.203 <0.0000000000000002 ***
ID -0.0001264 0.0008642 -0.146 0.884
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1886 on 81 degrees of freedom
Multiple R-squared: 0.0002639, Adjusted R-squared: -0.01208
F-statistic: 0.02138 on 1 and 81 DF, p-value: 0.8841
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 = 0.12077, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.41, df = 1, p-value = 0.001253
Box-Ljung test
data: lm_residuals
X-squared = 67.614, df = 1, p-value = 0.000000000000000222
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.63725 -0.15122 -0.03642 0.12556 0.70087
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.596628 0.074264 34.97 <0.0000000000000002 ***
ID -0.006458 0.002153 -3.00 0.004 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2816 on 57 degrees of freedom
Multiple R-squared: 0.1364, Adjusted R-squared: 0.1212
F-statistic: 8.999 on 1 and 57 DF, p-value: 0.004
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22034, p-value = 0.1141
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.17971, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 21.918, df = 1, p-value = 0.000002845
Box-Ljung test
data: lm_residuals
X-squared = 47.441, df = 1, p-value = 0.000000000005669
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.40272 -0.11923 -0.00419 0.12324 0.37229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7561741 0.0404980 43.364 <0.0000000000000002 ***
ID 0.0007889 0.0008687 0.908 0.367
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1794 on 78 degrees of freedom
Multiple R-squared: 0.01046, Adjusted R-squared: -0.002223
F-statistic: 0.8248 on 1 and 78 DF, p-value: 0.3666
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 = 0.11693, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.377, df = 1, p-value = 0.000006359
Box-Ljung test
data: lm_residuals
X-squared = 70.542, df = 1, p-value < 0.00000000000000022