Analysis
[1] "株式平均利回り:株式平均利回り:第一部: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 0.87 0.87
2000 0.86 0.84 0.84 0.85 0.85 0.85 0.86 0.94 0.95 0.99 1.02 1.04
2001 1.10 1.10 1.11 1.04 1.00 1.03 1.10 1.14 1.29 1.23 1.25 1.30
2002 1.33 1.35 1.24 1.24 1.21 1.24 1.20 1.26 1.29 1.36 1.39 1.42
2003 1.39 1.37 1.41 1.38 1.33 1.26 1.23 1.22 1.15 1.12 1.18 1.18
2004 1.12 1.13 1.04 0.98 1.06 1.00 1.11 1.15 1.14 1.15 1.17 1.16
2005 1.11 1.07 1.03 1.07 1.09 1.08 1.20 1.17 1.11 1.07 0.99 0.92
2006 0.88 0.90 0.91 0.88 0.93 1.03 1.19 1.16 1.16 1.16 1.21 1.16
2007 1.13 1.09 1.12 1.13 1.13 1.12 1.22 1.34 1.38 1.33 1.44 1.46
2008 1.67 1.67 1.78 1.71 1.62 1.61 1.79 1.85 1.98 2.50 2.50 2.52
2009 2.54 2.70 2.69 2.51 2.39 2.22 1.97 1.88 1.90 1.99 2.10 2.05
2010 1.98 2.03 1.93 1.81 1.82 1.83 1.88 1.95 1.94 1.99 1.97 1.84
2011 1.76 1.72 1.85 1.89 2.01 2.07 1.99 2.16 2.19 2.19 2.26 2.24
2012 2.18 2.07 1.96 2.03 2.24 2.30 2.24 2.23 2.26 2.27 2.22 2.08
2013 1.91 1.83 1.69 1.60 1.55 1.75 1.62 1.66 1.60 1.58 1.55 1.52
2014 1.47 1.57 1.59 1.62 1.72 1.67 1.62 1.61 1.56 1.64 1.52 1.48
2015 1.49 1.44 1.38 1.37 1.45 1.46 1.46 1.47 1.61 1.57 1.50 1.52
2016 1.67 1.77 1.70 1.74 1.83 1.93 1.93 1.95 1.90 1.84 1.79 1.68
2017 1.65 1.65 1.62 1.70 1.67 1.67 1.63 1.62 1.58 1.50 1.46 1.43
2018 1.37 1.47 1.50 1.48 1.53 1.61 1.66 1.69 1.67 1.72 1.77 1.89
2019 1.95 1.89 1.88 1.86 2.04 2.09 2.04 2.14 2.07 2.00
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.29517 -0.09078 0.01645 0.08841 0.22876
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.842456 0.040096 45.951 < 0.0000000000000002 ***
ID 0.009595 0.001747 5.492 0.00000306 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1228 on 37 degrees of freedom
Multiple R-squared: 0.4491, Adjusted R-squared: 0.4342
F-statistic: 30.16 on 1 and 37 DF, p-value: 0.000003063
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 = 0.45337, p-value = 0.0000000002291
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1408, df = 1, p-value = 0.2855
Box-Ljung test
data: lm_residuals
X-squared = 25.029, df = 1, p-value = 0.0000005647
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35951 -0.13007 -0.01357 0.11196 0.51914
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5580958 0.0391454 39.803 < 0.0000000000000002 ***
ID 0.0027648 0.0008096 3.415 0.000999 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1767 on 81 degrees of freedom
Multiple R-squared: 0.1259, Adjusted R-squared: 0.1151
F-statistic: 11.66 on 1 and 81 DF, p-value: 0.0009988
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12048, p-value = 0.5863
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.17217, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.9883, df = 1, p-value = 0.3202
Box-Ljung test
data: lm_residuals
X-squared = 62.412, df = 1, p-value = 0.000000000000002776
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.45293 -0.18436 -0.06232 0.15889 0.63493
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0621216 0.0677523 30.436 <0.0000000000000002 ***
ID 0.0002683 0.0019640 0.137 0.892
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2569 on 57 degrees of freedom
Multiple R-squared: 0.0003272, Adjusted R-squared: -0.01721
F-statistic: 0.01866 on 1 and 57 DF, p-value: 0.8918
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 = 0.21667, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 16.121, df = 1, p-value = 0.00005941
Box-Ljung test
data: lm_residuals
X-squared = 46.642, df = 1, p-value = 0.00000000000852
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36583 -0.10807 -0.00276 0.10499 0.33066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5075475 0.0361649 41.685 < 0.0000000000000002 ***
ID 0.0038692 0.0007757 4.988 0.00000362 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1602 on 78 degrees of freedom
Multiple R-squared: 0.2418, Adjusted R-squared: 0.2321
F-statistic: 24.88 on 1 and 78 DF, p-value: 0.000003615
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1625, p-value = 0.2424
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.18892, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.347, df = 1, p-value = 0.0007559
Box-Ljung test
data: lm_residuals
X-squared = 65.659, df = 1, p-value = 0.0000000000000005551