Analysis
[1] "景気動向指数個別系列:先行系列:マネーストック(M2)(前年同月比)(%):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 2.6
2000 2.6 2.1 1.9 2.9 2.2 1.9 2.0 1.8 2.0 2.1 1.9 2.0
2001 2.2 2.6 2.5 2.3 2.6 2.9 3.0 2.9 3.2 2.9 3.0 3.3
2002 3.5 3.5 3.7 3.5 3.5 3.4 3.3 3.4 3.2 3.3 3.2 2.2
2003 1.9 1.9 1.7 1.3 1.6 1.8 1.8 1.9 1.8 1.5 1.5 1.5
2004 1.6 1.7 1.7 1.8 2.0 1.7 1.8 1.9 2.1 2.0 2.0 2.0
2005 1.9 1.8 2.0 1.8 1.4 1.6 1.6 1.6 2.0 1.9 2.0 1.9
2006 1.8 1.7 1.4 1.6 1.3 1.1 0.5 0.4 0.5 0.5 0.6 0.7
2007 0.9 1.0 1.1 1.1 1.4 1.8 2.0 1.8 1.7 1.9 2.0 2.1
2008 2.1 2.4 2.3 1.9 2.1 2.2 2.1 2.4 2.2 1.8 1.8 1.8
2009 2.0 2.1 2.2 2.7 2.7 2.5 2.7 2.8 3.0 3.4 3.3 3.1
2010 3.0 2.7 2.7 2.9 3.1 2.9 2.7 2.8 2.8 2.8 2.6 2.3
2011 2.3 2.4 2.6 2.7 2.7 2.8 2.9 2.7 2.7 2.8 3.0 3.1
2012 3.0 2.9 3.0 2.6 2.2 2.3 2.3 2.4 2.4 2.3 2.1 2.6
2013 2.7 2.9 3.0 3.2 3.5 3.8 3.7 3.8 3.8 4.1 4.3 4.2
2014 4.3 4.0 3.5 3.4 3.3 3.0 3.0 3.0 3.0 3.1 3.6 3.5
2015 3.3 3.5 3.6 3.6 4.1 3.8 3.9 4.1 3.8 3.6 3.3 3.1
2016 3.1 3.1 3.0 3.3 3.3 3.3 3.3 3.2 3.4 3.6 3.8 3.9
2017 3.9 4.1 4.2 3.9 3.8 3.9 4.0 4.0 4.0 4.1 4.0 3.6
2018 3.4 3.2 3.1 3.2 3.2 3.1 2.9 2.9 2.8 2.7 2.3 2.4
2019 2.3 2.3 2.4 2.5 2.6 2.3 2.3 2.4 2.4
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.49261 -0.18449 -0.01617 0.16996 0.49273
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.024291 0.083717 36.125 < 0.0000000000000002 ***
ID -0.015445 0.003648 -4.234 0.000146 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2564 on 37 degrees of freedom
Multiple R-squared: 0.3264, Adjusted R-squared: 0.3082
F-statistic: 17.93 on 1 and 37 DF, p-value: 0.0001456
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 = 0.52804, p-value = 0.000000003867
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.24638, df = 1, p-value = 0.6196
Box-Ljung test
data: lm_residuals
X-squared = 20.501, df = 1, p-value = 0.000005959
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.08976 -0.36712 -0.02482 0.40766 0.95145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.800586 0.112603 33.752 < 0.0000000000000002 ***
ID -0.010824 0.002386 -4.537 0.0000201 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.502 on 79 degrees of freedom
Multiple R-squared: 0.2067, Adjusted R-squared: 0.1967
F-statistic: 20.58 on 1 and 79 DF, p-value: 0.0000201
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.098765, p-value = 0.8277
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.14931, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.50754, df = 1, p-value = 0.4762
Box-Ljung test
data: lm_residuals
X-squared = 66.362, df = 1, p-value = 0.0000000000000003331
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.66671 -0.31892 0.05312 0.21274 0.87116
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.417008 0.095783 25.234 <0.0000000000000002 ***
ID 0.006213 0.002777 2.238 0.0292 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3632 on 57 degrees of freedom
Multiple R-squared: 0.08074, Adjusted R-squared: 0.06461
F-statistic: 5.007 on 1 and 57 DF, p-value: 0.02918
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20339, p-value = 0.1748
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.30072, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.5303, df = 1, p-value = 0.1117
Box-Ljung test
data: lm_residuals
X-squared = 43.74, df = 1, p-value = 0.00000000003751
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.70972 -0.36086 -0.07108 0.34937 0.94026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.914252 0.107988 36.247 < 0.0000000000000002 ***
ID -0.013636 0.002375 -5.741 0.000000183 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4723 on 76 degrees of freedom
Multiple R-squared: 0.3025, Adjusted R-squared: 0.2933
F-statistic: 32.96 on 1 and 76 DF, p-value: 0.000000183
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.24359, p-value = 0.01923
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.16945, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.8783, df = 1, p-value = 0.08978
Box-Ljung test
data: lm_residuals
X-squared = 64.886, df = 1, p-value = 0.0000000000000007772