Analysis
[1] "マネタリーベース:マネタリーベース平均残高/うち貨幣流通高(前年比):%:日本銀行"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 1.6 1.6 1.4 1.3 1.1 1.1 1.0 1.9 1.8 1.6 1.4 0.9
2001 0.9 1.2 1.4 1.5 1.6 1.7 1.9 1.0 1.0 1.2 1.3 1.6
2002 1.7 1.6 1.5 1.6 1.9 2.0 2.0 2.0 2.1 2.1 2.1 1.9
2003 1.7 1.9 2.0 1.7 1.2 1.2 1.3 1.3 1.4 1.4 1.4 1.4
2004 1.3 1.4 1.5 1.5 1.7 1.9 1.9 1.9 1.9 2.0 1.9 1.7
2005 1.6 1.9 2.0 1.9 1.6 1.2 0.8 0.6 0.5 0.4 0.4 0.3
2006 0.3 0.1 0.0 0.2 0.4 0.6 0.7 0.8 0.8 0.8 0.7 0.6
2007 0.7 0.8 0.9 0.8 0.7 0.7 0.7 0.7 0.8 0.9 0.9 1.0
2008 1.0 1.0 1.0 0.9 0.8 0.8 0.8 0.7 0.7 0.6 0.5 0.4
2009 0.2 0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.2 -0.4 -0.7
2010 -0.7 -0.7 -0.6 -0.5 -0.4 -0.3 -0.4 -0.3 -0.4 -0.5 -0.4 -0.4
2011 -0.2 -0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.2
2012 0.1 0.1 0.1 -0.1 -0.1 0.0 0.2 0.3 0.4 0.4 0.4 0.5
2013 0.6 0.7 0.7 0.8 0.9 1.0 1.0 1.0 1.0 1.1 1.1 1.1
2014 1.1 1.0 1.1 1.1 1.0 1.0 1.0 0.9 0.9 0.9 0.8 0.7
2015 0.7 0.8 0.9 0.8 0.7 0.7 0.6 0.6 0.7 0.9 0.9 0.8
2016 0.7 0.7 0.8 0.9 1.0 1.0 1.0 1.1 1.0 0.9 1.0 1.1
2017 1.1 1.1 1.0 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.1
2018 1.1 1.2 1.2 1.1 1.0 1.0 0.9 0.9 1.0 1.0 1.1 1.1
2019 1.2 1.4 1.7 2.0 2.3 2.3 2.3 2.2 2.2 2.2 2.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.28923 -0.10538 0.01385 0.09077 0.39769
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.623077 0.048831 -12.76 0.00000000000000405 ***
ID 0.025385 0.002128 11.93 0.00000000000003031 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1496 on 37 degrees of freedom
Multiple R-squared: 0.7937, Adjusted R-squared: 0.7881
F-statistic: 142.3 on 1 and 37 DF, p-value: 0.00000000000003031
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 = 0.55459, p-value = 0.00000000944
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.8362, df = 1, p-value = 0.1754
Box-Ljung test
data: lm_residuals
X-squared = 15.99, df = 1, p-value = 0.00006368
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.48639 -0.20870 -0.08642 0.19675 0.81364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.631061 0.069538 9.075 0.0000000000000564 ***
ID 0.011108 0.001438 7.724 0.0000000000262318 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3139 on 81 degrees of freedom
Multiple R-squared: 0.4241, Adjusted R-squared: 0.417
F-statistic: 59.66 on 1 and 81 DF, p-value: 0.00000000002623
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 = 0.089531, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.531, df = 1, p-value = 0.000005868
Box-Ljung test
data: lm_residuals
X-squared = 74.237, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.72033 -0.22552 -0.04853 0.28291 0.81085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.012332 0.108854 -0.113 0.91
ID 0.001485 0.003156 0.470 0.64
Residual standard error: 0.4128 on 57 degrees of freedom
Multiple R-squared: 0.003868, Adjusted R-squared: -0.01361
F-statistic: 0.2213 on 1 and 57 DF, p-value: 0.6398
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 = 0.066977, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 9.9199, df = 1, p-value = 0.001635
Box-Ljung test
data: lm_residuals
X-squared = 51.675, df = 1, p-value = 0.0000000000006549
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.48680 -0.21413 -0.08604 0.21511 0.81289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.662373 0.072179 9.177 0.000000000000049 ***
ID 0.011145 0.001548 7.199 0.000000000329150 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3198 on 78 degrees of freedom
Multiple R-squared: 0.3992, Adjusted R-squared: 0.3915
F-statistic: 51.82 on 1 and 78 DF, p-value: 0.0000000003291
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25, p-value = 0.01319
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.087589, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.843, df = 1, p-value = 0.00001419
Box-Ljung test
data: lm_residuals
X-squared = 71.671, df = 1, p-value < 0.00000000000000022