Analysis
[1] "株式平均利回り:株式平均利回り:第二部: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 0.54 0.56
2000 0.64 0.59 0.68 0.77 0.79 0.82 0.87 0.95 0.98 1.04 1.10 1.19
2001 1.26 1.20 1.31 1.28 1.21 1.25 1.35 1.39 1.57 1.54 1.56 1.67
2002 1.70 1.74 1.70 1.68 1.62 1.60 1.49 1.55 1.74 1.84 1.96 2.01
2003 1.94 1.88 1.98 1.93 1.83 1.74 1.64 1.56 1.49 1.44 1.53 1.58
2004 1.46 1.42 1.29 1.09 1.15 1.20 1.25 1.32 1.30 1.38 1.41 1.43
2005 1.31 1.23 1.08 1.11 1.10 1.10 1.18 1.18 1.12 1.06 0.98 0.89
2006 0.84 0.87 0.92 0.90 0.96 1.06 1.24 1.23 1.28 1.31 1.34 1.30
2007 1.30 1.27 1.30 1.34 1.37 1.34 1.37 1.48 1.58 1.57 1.67 1.72
2008 1.92 1.94 2.03 2.04 1.93 1.93 2.12 2.27 2.41 2.90 2.96 3.02
2009 2.97 3.10 3.15 2.98 2.86 2.60 2.27 2.18 2.16 2.24 2.37 2.41
2010 2.32 2.33 2.24 2.09 2.04 2.03 2.08 2.13 2.14 2.21 2.20 2.08
2011 1.97 1.90 1.98 2.00 2.06 2.10 2.07 2.22 2.23 2.25 2.32 2.28
2012 2.26 2.18 2.07 2.10 2.25 2.34 2.31 2.34 2.35 2.37 2.34 2.20
2013 1.99 1.91 1.76 1.68 1.64 1.84 1.75 1.73 1.69 1.63 1.63 1.63
2014 1.52 1.58 1.60 1.63 1.75 1.72 1.64 1.62 1.54 1.58 1.53 1.45
2015 1.42 1.41 1.36 1.32 1.33 1.33 1.31 1.36 1.47 1.45 1.43 1.44
2016 1.54 1.63 1.59 1.62 1.66 1.73 1.73 1.63 1.53 1.48 1.42 1.30
2017 1.21 1.17 1.10 1.13 1.15 1.17 1.14 1.01 1.00 0.96 0.96 1.03
2018 0.97 1.00 1.01 1.03 1.06 1.10 1.11 1.13 1.16 1.20 1.21 1.32
2019 1.34 1.33 1.32 1.30 1.53 1.68 1.66 1.73 1.71 1.64
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.28379 -0.10858 0.02361 0.10005 0.26073
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.139406 0.042816 49.968 <0.0000000000000002 ***
ID 0.002466 0.001866 1.322 0.194
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1311 on 37 degrees of freedom
Multiple R-squared: 0.04508, Adjusted R-squared: 0.01927
F-statistic: 1.747 on 1 and 37 DF, p-value: 0.1944
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 = 0.3193, p-value = 0.0000000000002655
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.2632, df = 1, p-value = 0.1325
Box-Ljung test
data: lm_residuals
X-squared = 29.094, df = 1, p-value = 0.00000006894
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36385 -0.17238 -0.02076 0.11463 0.55302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.717752 0.048667 35.296 < 0.0000000000000002 ***
ID -0.006676 0.001006 -6.633 0.00000000341 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2197 on 81 degrees of freedom
Multiple R-squared: 0.352, Adjusted R-squared: 0.344
F-statistic: 44 on 1 and 81 DF, p-value: 0.000000003411
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.084337, p-value = 0.9317
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.10897, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 15.964, df = 1, p-value = 0.00006456
Box-Ljung test
data: lm_residuals
X-squared = 67.526, df = 1, p-value = 0.000000000000000222
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.55802 -0.19321 -0.05677 0.15118 0.73246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.502116 0.077684 32.21 < 0.0000000000000002 ***
ID -0.007048 0.002252 -3.13 0.00276 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2946 on 57 degrees of freedom
Multiple R-squared: 0.1466, Adjusted R-squared: 0.1317
F-statistic: 9.795 on 1 and 57 DF, p-value: 0.002758
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10169, p-value = 0.9239
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.17444, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 19.041, df = 1, p-value = 0.0000128
Box-Ljung test
data: lm_residuals
X-squared = 49.008, df = 1, p-value = 0.000000000002549
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36650 -0.16580 0.00296 0.10386 0.52912
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.646247 0.047757 34.471 < 0.0000000000000002 ***
ID -0.005710 0.001024 -5.574 0.000000343 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2116 on 78 degrees of freedom
Multiple R-squared: 0.2849, Adjusted R-squared: 0.2757
F-statistic: 31.07 on 1 and 78 DF, p-value: 0.0000003434
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.2, p-value = 0.08141
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.10253, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 24.667, df = 1, p-value = 0.0000006816
Box-Ljung test
data: lm_residuals
X-squared = 69.921, df = 1, p-value < 0.00000000000000022