Analysis
[1] "株式平均利回り:株式平均利回り:第二部: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 0.91 0.99
2000 1.00 0.87 1.00 1.06 1.12 1.10 1.25 1.33 1.42 1.56 1.58 1.65
2001 1.62 1.60 1.68 1.64 1.61 1.60 1.79 1.85 2.01 1.95 2.01 2.12
2002 2.18 2.16 2.17 2.14 2.07 2.12 2.06 2.13 2.24 2.37 2.49 2.52
2003 2.47 2.42 2.52 2.39 2.33 2.16 2.09 2.03 1.99 1.90 2.02 1.99
2004 1.84 1.78 1.64 1.48 1.57 1.51 1.80 1.73 1.85 1.87 1.87 1.86
2005 1.74 1.62 1.50 1.51 1.53 1.55 1.51 1.49 1.43 1.39 1.29 1.18
2006 1.14 1.13 1.16 1.19 1.28 1.43 1.51 1.46 1.50 1.52 1.57 1.53
2007 1.52 1.49 1.52 1.55 1.54 1.56 1.57 1.71 1.76 1.77 1.90 1.95
2008 2.13 2.10 2.24 2.17 2.07 2.22 2.31 2.46 2.69 3.12 3.16 3.18
2009 3.26 3.41 3.33 3.27 3.10 2.68 2.65 2.57 2.61 2.68 2.88 2.76
2010 2.72 2.71 2.56 2.42 2.54 2.55 2.52 2.59 2.59 2.67 2.61 2.50
2011 2.40 2.32 2.38 2.44 2.54 2.47 2.51 2.62 2.66 2.68 2.74 2.65
2012 2.59 2.44 2.39 2.46 2.61 2.55 2.58 2.58 2.62 2.63 2.57 2.40
2013 2.24 2.19 2.05 1.95 2.02 2.13 2.14 2.16 2.03 2.03 2.02 1.99
2014 1.91 1.95 1.98 2.01 2.10 2.00 1.93 1.92 1.89 1.93 1.85 1.82
2015 1.83 1.80 1.77 1.75 1.80 1.78 1.77 1.87 1.93 1.88 1.89 1.91
2016 1.98 2.08 2.04 2.09 2.21 2.32 2.23 2.23 2.21 2.15 2.07 2.01
2017 1.95 1.86 1.86 1.88 1.92 1.88 1.80 1.76 1.77 1.71 1.67 1.64
2018 1.55 1.60 1.66 1.68 1.76 1.78 1.83 1.84 1.81 2.00 2.03 2.27
2019 2.16 2.10 2.13 2.12 2.38 2.33 2.32 2.42 2.37 2.28
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.257347 -0.077531 0.008864 0.084627 0.268706
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.618084 0.037693 69.457 <0.0000000000000002 ***
ID -0.002263 0.001642 -1.378 0.177
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1154 on 37 degrees of freedom
Multiple R-squared: 0.04881, Adjusted R-squared: 0.0231
F-statistic: 1.899 on 1 and 37 DF, p-value: 0.1765
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.5622, p-value = 0.00000001208
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.5421, df = 1, p-value = 0.2143
Box-Ljung test
data: lm_residuals
X-squared = 21.646, df = 1, p-value = 0.000003279
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.43203 -0.15069 -0.02693 0.14167 0.43627
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.97648545 0.04493423 43.986 <0.0000000000000002 ***
ID 0.00008942 0.00092930 0.096 0.924
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2028 on 81 degrees of freedom
Multiple R-squared: 0.0001143, Adjusted R-squared: -0.01223
F-statistic: 0.009258 on 1 and 81 DF, p-value: 0.9236
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 = 0.14458, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 14.808, df = 1, p-value = 0.000119
Box-Ljung test
data: lm_residuals
X-squared = 67.791, df = 1, p-value = 0.000000000000000222
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.71544 -0.11136 -0.03407 0.10314 0.67643
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.796967 0.072081 38.803 <0.0000000000000002 ***
ID -0.005763 0.002090 -2.758 0.0078 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2733 on 57 degrees of freedom
Multiple R-squared: 0.1178, Adjusted R-squared: 0.1023
F-statistic: 7.608 on 1 and 57 DF, p-value: 0.007798
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.20957, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 21.17, df = 1, p-value = 0.000004202
Box-Ljung test
data: lm_residuals
X-squared = 43.944, df = 1, p-value = 0.00000000003379
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.43707 -0.15331 -0.00606 0.12021 0.41450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.9298449 0.0442327 43.629 <0.0000000000000002 ***
ID 0.0009699 0.0009488 1.022 0.31
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.196 on 78 degrees of freedom
Multiple R-squared: 0.01322, Adjusted R-squared: 0.000569
F-statistic: 1.045 on 1 and 78 DF, p-value: 0.3098
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.05, p-value = 1
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.1448, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 21.477, df = 1, p-value = 0.000003582
Box-Ljung test
data: lm_residuals
X-squared = 69.192, df = 1, p-value < 0.00000000000000022