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:兆円:日本銀行"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 4.1749 4.1270 4.1009 4.1052 4.1183 4.0955 4.0914 4.1439 4.1391 4.1352 4.1465 4.1939
2001 4.2134 4.1773 4.1579 4.1687 4.1857 4.1665 4.1691 4.1834 4.1797 4.1832 4.1996 4.2604
2002 4.2843 4.2446 4.2216 4.2374 4.2649 4.2506 4.2522 4.2653 4.2663 4.2692 4.2876 4.3398
2003 4.3584 4.3246 4.3051 4.3077 4.3143 4.3016 4.3059 4.3207 4.3240 4.3303 4.3485 4.3990
2004 4.4148 4.3851 4.3681 4.3715 4.3889 4.3829 4.3871 4.4011 4.4061 4.4163 4.4309 4.4734
2005 4.4871 4.4683 4.4540 4.4529 4.4573 4.4349 4.4233 4.4288 4.4299 4.4345 4.4491 4.4859
2006 4.4987 4.4724 4.4521 4.4619 4.4760 4.4600 4.4555 4.4647 4.4658 4.4691 4.4822 4.5143
2007 4.5280 4.5061 4.4902 4.4972 4.5064 4.4914 4.4884 4.4972 4.5002 4.5071 4.5215 4.5575
2008 4.5739 4.5513 4.5363 4.5371 4.5404 4.5281 4.5232 4.5280 4.5301 4.5329 4.5434 4.5770
2009 4.5846 4.5568 4.5308 4.5280 4.5326 4.5181 4.5142 4.5186 4.5240 4.5248 4.5270 4.5462
2010 4.5509 4.5233 4.5041 4.5067 4.5153 4.5032 4.4979 4.5033 4.5037 4.5040 4.5096 4.5294
2011 4.5400 4.5171 4.5022 4.5124 4.5187 4.5013 4.4958 4.5028 4.5034 4.5051 4.5106 4.5364
2012 4.5466 4.5215 4.5052 4.5087 4.5149 4.5031 4.5044 4.5143 4.5196 4.5212 4.5285 4.5605
2013 4.5742 4.5522 4.5368 4.5433 4.5549 4.5480 4.5498 4.5611 4.5661 4.5693 4.5774 4.6101
2014 4.6233 4.5994 4.5870 4.5936 4.6013 4.5950 4.5962 4.6025 4.6053 4.6085 4.6161 4.6430
2015 4.6545 4.6378 4.6280 4.6292 4.6335 4.6270 4.6254 4.6302 4.6397 4.6488 4.6563 4.6790
2016 4.6849 4.6694 4.6642 4.6706 4.6799 4.6732 4.6735 4.6808 4.6874 4.6917 4.7042 4.7307
2017 4.7363 4.7193 4.7121 4.7204 4.7319 4.7274 4.7282 4.7386 4.7443 4.7485 4.7595 4.7817
2018 4.7878 4.7741 4.7672 4.7735 4.7812 4.7729 4.7720 4.7830 4.7899 4.7978 4.8095 4.8326
2019 4.8463 4.8433 4.8466 4.8696 4.8918 4.8850 4.8831 4.8876 4.8947 4.9045 4.9147
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.020760 -0.012538 -0.004078 0.008012 0.043473
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.51595574 0.00528472 854.531 <0.0000000000000002 ***
ID 0.00002747 0.00023028 0.119 0.906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01619 on 37 degrees of freedom
Multiple R-squared: 0.0003844, Adjusted R-squared: -0.02663
F-statistic: 0.01423 on 1 and 37 DF, p-value: 0.9057
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23077, p-value = 0.2523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.6926, p-value = 0.0000004785
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.017988, df = 1, p-value = 0.8933
Box-Ljung test
data: lm_residuals
X-squared = 12.831, df = 1, p-value = 0.000341
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.029638 -0.016735 -0.004252 0.009954 0.048404
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.52186001 0.00459939 983.1 <0.0000000000000002 ***
ID 0.00417579 0.00009512 43.9 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02076 on 81 degrees of freedom
Multiple R-squared: 0.9597, Adjusted R-squared: 0.9592
F-statistic: 1927 on 1 and 81 DF, p-value: < 0.00000000000000022
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 = 0.27958, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 6.0358, df = 1, p-value = 0.01402
Box-Ljung test
data: lm_residuals
X-squared = 54.563, df = 1, p-value = 0.0000000000001505
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.026831 -0.014782 -0.003071 0.007712 0.056490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.5310498 0.0053065 853.873 <0.0000000000000002 ***
ID -0.0002340 0.0001538 -1.521 0.134
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02012 on 57 degrees of freedom
Multiple R-squared: 0.03902, Adjusted R-squared: 0.02216
F-statistic: 2.315 on 1 and 57 DF, p-value: 0.1337
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 = 0.47169, p-value = 0.00000000000007107
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.65402, df = 1, p-value = 0.4187
Box-Ljung test
data: lm_residuals
X-squared = 35.266, df = 1, p-value = 0.000000002876
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.030415 -0.017190 -0.002558 0.010239 0.050247
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.53057791 0.00455921 993.72 <0.0000000000000002 ***
ID 0.00424746 0.00009779 43.43 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0202 on 78 degrees of freedom
Multiple R-squared: 0.9603, Adjusted R-squared: 0.9598
F-statistic: 1886 on 1 and 78 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1375, p-value = 0.4383
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.27285, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.6972, df = 1, p-value = 0.005531
Box-Ljung test
data: lm_residuals
X-squared = 57.422, df = 1, p-value = 0.00000000000003519