Analysis
[1] "景気動向指数個別系列:先行系列:消費者態度指数:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 37.0
2000 37.3 37.5 37.8 38.0 38.3 38.5 38.7 38.9 39.1 39.0 38.8 38.7
2001 37.4 36.1 34.8 34.9 35.0 35.1 33.6 32.1 30.6 30.6 31.9 30.6
2002 33.7 31.9 35.2 35.8 37.4 36.7 37.6 37.3 35.9 34.9 35.3 33.2
2003 33.5 33.7 33.1 35.0 35.6 34.8 36.2 38.3 38.1 39.9 39.6 37.8
2004 38.8 39.7 41.0 41.9 44.9 41.9 45.3 45.6 42.9 44.3 44.5 41.2
2005 44.1 44.2 42.1 44.1 44.8 43.5 44.8 45.0 42.4 44.5 44.8 43.4
2006 46.0 46.4 44.8 46.7 46.4 44.0 45.2 44.4 43.3 45.0 45.5 43.0
2007 45.0 45.2 43.9 44.2 44.1 42.0 41.5 41.0 41.0 39.9 37.2 35.6
2008 35.2 33.8 34.4 32.9 31.7 30.6 29.4 28.4 29.6 27.7 26.7 24.8
2009 25.1 25.7 27.5 30.9 33.7 35.4 36.9 37.6 37.8 37.9 37.1 35.2
2010 36.6 37.2 38.1 39.1 39.7 40.5 40.3 39.5 38.5 38.2 37.7 37.4
2011 38.2 37.8 35.6 31.2 32.6 33.7 35.0 34.9 35.8 35.8 35.0 35.4
2012 36.6 36.3 37.3 37.5 37.8 37.9 37.7 37.7 37.6 37.0 36.3 36.3
2013 40.3 41.1 41.8 44.4 45.6 44.3 43.6 43.0 45.1 40.5 41.3 40.0
2014 39.7 37.6 36.9 36.6 39.2 40.5 41.1 40.5 39.6 37.8 36.7 37.5
2015 38.4 39.8 41.1 41.1 41.4 41.9 40.5 41.7 40.4 40.7 41.4 41.3
2016 41.7 39.3 41.3 40.2 40.8 42.1 41.3 42.3 42.6 41.7 40.3 42.3
2017 42.7 42.4 44.0 42.7 43.5 43.5 43.8 43.4 43.9 43.8 44.0 44.1
2018 44.3 43.7 44.1 43.0 43.9 43.9 43.3 43.2 43.2 42.7 42.3 42.2
2019 41.8 40.9 40.4 39.9 39.4 38.8 37.7 37.1 35.8
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-5.764 -0.969 0.272 1.291 3.126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.74291 0.61884 60.99 <0.0000000000000002 ***
ID -0.04099 0.02697 -1.52 0.137
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.895 on 37 degrees of freedom
Multiple R-squared: 0.05878, Adjusted R-squared: 0.03335
F-statistic: 2.311 on 1 and 37 DF, p-value: 0.137
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20513, p-value = 0.3888
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.34966, p-value = 0.00000000000158
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.50449, df = 1, p-value = 0.4775
Box-Ljung test
data: lm_residuals
X-squared = 28.635, df = 1, p-value = 0.00000008738
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-6.0379 -1.1688 0.1266 1.6140 4.5871
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.95861 0.49477 82.783 <0.0000000000000002 ***
ID 0.01086 0.01048 1.036 0.304
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.206 on 79 degrees of freedom
Multiple R-squared: 0.01339, Adjusted R-squared: 0.0009047
F-statistic: 1.072 on 1 and 79 DF, p-value: 0.3036
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14815, p-value = 0.338
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.2773, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.038484, df = 1, p-value = 0.8445
Box-Ljung test
data: lm_residuals
X-squared = 55.594, df = 1, p-value = 0.00000000000008915
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-7.1243 -1.9585 -0.4688 2.6180 5.8138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.69673 0.87467 35.095 < 0.0000000000000002 ***
ID 0.15344 0.02536 6.052 0.000000118 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.317 on 57 degrees of freedom
Multiple R-squared: 0.3912, Adjusted R-squared: 0.3805
F-statistic: 36.62 on 1 and 57 DF, p-value: 0.0000001183
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.084746, p-value = 0.9854
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.1659, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.837, df = 1, p-value = 0.0005807
Box-Ljung test
data: lm_residuals
X-squared = 51.738, df = 1, p-value = 0.000000000000634
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-6.0434 -1.1834 0.1827 1.7782 4.5990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.97885 0.51349 79.805 <0.0000000000000002 ***
ID 0.01108 0.01129 0.981 0.33
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.246 on 76 degrees of freedom
Multiple R-squared: 0.01251, Adjusted R-squared: -0.0004787
F-statistic: 0.9632 on 1 and 76 DF, p-value: 0.3295
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 = 0.316
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.25777, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.034965, df = 1, p-value = 0.8517
Box-Ljung test
data: lm_residuals
X-squared = 52.947, df = 1, p-value = 0.0000000000003426