Analysis
[1] "マネタリーベース:マネタリーベース平均残高/うち日本銀行券発行高(前年比):%:日本銀行"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 11.3 7.5 8.0 10.4 7.8 6.6 5.7 5.6 6.6 7.9 8.5 3.8
2001 4.1 6.6 5.5 3.8 6.1 8.9 9.6 8.9 8.7 7.7 8.0 8.1
2002 10.0 12.4 14.6 16.2 15.9 14.6 12.4 14.0 13.5 12.6 11.8 10.0
2003 9.4 8.3 6.3 4.5 4.7 5.2 5.3 5.2 5.1 4.7 4.4 3.3
2004 2.6 2.7 1.7 1.6 1.7 1.0 1.1 1.2 2.2 2.1 3.6 2.4
2005 2.6 2.5 3.7 4.5 4.0 3.5 3.4 3.1 2.6 2.9 1.1 1.9
2006 1.9 2.3 1.5 0.4 0.6 0.9 0.7 1.1 0.9 0.9 0.9 0.5
2007 0.7 1.3 1.3 1.3 1.1 1.7 1.6 1.3 1.5 1.7 1.7 1.7
2008 1.5 1.1 0.8 0.6 0.7 0.7 0.6 0.9 0.4 0.8 1.1 0.5
2009 0.4 0.7 0.8 1.0 1.1 0.9 0.7 0.5 1.0 0.0 -0.3 -0.3
2010 -0.2 0.1 0.4 0.9 0.6 0.7 1.0 1.2 1.0 1.5 1.8 2.0
2011 2.0 2.4 3.7 3.7 2.9 2.7 2.7 2.7 2.9 2.6 2.4 2.3
2012 2.3 2.2 0.9 0.9 1.6 2.2 2.3 2.4 2.3 2.6 2.7 2.8
2013 3.2 3.1 3.0 3.0 3.1 3.3 3.1 3.3 3.4 3.4 3.6 3.7
2014 3.9 3.8 4.0 3.5 3.4 3.4 3.4 3.5 3.5 3.6 3.7 3.6
2015 3.5 3.7 3.6 4.1 4.5 4.7 5.0 5.3 5.9 6.1 6.2 6.1
2016 6.2 6.6 6.7 6.8 6.5 6.2 5.9 5.7 5.2 5.0 4.7 4.5
2017 4.5 4.5 4.5 4.2 4.4 4.6 4.6 4.7 4.6 4.7 4.8 4.4
2018 4.4 4.6 4.3 4.3 4.0 4.2 4.1 3.8 4.0 3.8 3.5 3.4
2019 3.3 3.2 3.3 4.6 4.6 3.1 2.8 2.7 2.4 2.3 2.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.5733 -0.4265 -0.1434 0.5086 2.0709
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.46019 0.26571 1.732 0.0916 .
ID 0.06494 0.01158 5.609 0.00000213 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8138 on 37 degrees of freedom
Multiple R-squared: 0.4595, Adjusted R-squared: 0.4449
F-statistic: 31.46 on 1 and 37 DF, p-value: 0.000002127
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15385, p-value = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.26025, p-value = 0.000000000000004418
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.00024804, df = 1, p-value = 0.9874
Box-Ljung test
data: lm_residuals
X-squared = 31.375, df = 1, p-value = 0.00000002127
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-2.0407 -0.7717 -0.1732 0.4361 2.6020
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1583015 0.2397486 17.34 <0.0000000000000002 ***
ID 0.0009928 0.0049583 0.20 0.842
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.082 on 81 degrees of freedom
Multiple R-squared: 0.0004947, Adjusted R-squared: -0.01184
F-statistic: 0.04009 on 1 and 81 DF, p-value: 0.8418
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14458, p-value = 0.3526
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.081394, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.19229, df = 1, p-value = 0.661
Box-Ljung test
data: lm_residuals
X-squared = 74.815, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.4757 -0.3332 0.0640 0.3751 1.9298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.13986 0.18731 0.747 0.458
ID 0.04658 0.00543 8.579 0.00000000000758 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7102 on 57 degrees of freedom
Multiple R-squared: 0.5635, Adjusted R-squared: 0.5559
F-statistic: 73.6 on 1 and 57 DF, p-value: 0.000000000007576
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.31863, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.18887, df = 1, p-value = 0.6639
Box-Ljung test
data: lm_residuals
X-squared = 43.355, df = 1, p-value = 0.00000000004565
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.9584 -0.7926 -0.1329 0.4394 2.5514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.326171 0.243733 17.750 <0.0000000000000002 ***
ID -0.002097 0.005228 -0.401 0.689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.08 on 78 degrees of freedom
Multiple R-squared: 0.002058, Adjusted R-squared: -0.01074
F-statistic: 0.1609 on 1 and 78 DF, p-value: 0.6895
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1125, p-value = 0.6953
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.084632, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.014471, df = 1, p-value = 0.9042
Box-Ljung test
data: lm_residuals
X-squared = 71.351, df = 1, p-value < 0.00000000000000022