Analysis
[1] "労働力調査(主要項目):完全失業率(%):季節調整値:男:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 4.8
2000 4.9 5.1 5.1 4.9 4.7 4.8 4.9 4.8 4.8 4.9 4.9 4.9
2001 4.9 4.8 4.9 5.1 5.1 5.1 5.2 5.2 5.4 5.6 5.6 5.7
2002 5.4 5.4 5.4 5.5 5.6 5.6 5.5 5.8 5.7 5.7 5.5 5.6
2003 5.5 5.4 5.7 5.7 5.7 5.7 5.4 5.3 5.5 5.3 5.3 5.1
2004 5.1 5.2 5.0 5.0 4.8 4.9 5.2 4.9 4.9 4.8 4.7 4.6
2005 4.7 4.8 4.7 4.6 4.7 4.5 4.5 4.4 4.3 4.5 4.6 4.5
2006 4.7 4.4 4.4 4.3 4.2 4.3 4.2 4.3 4.3 4.3 4.2 4.2
2007 4.1 4.1 4.1 4.0 3.9 3.8 3.8 3.7 4.0 4.0 3.9 3.8
2008 4.0 4.1 3.9 3.9 4.1 4.1 4.0 4.2 4.1 4.0 4.1 4.5
2009 4.4 4.6 4.9 5.2 5.4 5.5 5.9 5.7 5.6 5.4 5.5 5.3
2010 5.3 5.4 5.5 5.4 5.5 5.5 5.4 5.4 5.6 5.4 5.4 5.2
2011 5.2 4.9 5.0 5.0 5.0 4.9 5.0 4.7 4.5 4.8 4.7 4.9
2012 4.7 4.7 4.8 4.8 4.6 4.6 4.6 4.5 4.5 4.3 4.3 4.5
2013 4.5 4.6 4.5 4.4 4.3 4.2 4.2 4.4 4.2 4.2 4.1 3.8
2014 3.8 3.7 3.8 3.9 3.7 3.9 3.8 3.7 3.7 3.8 3.7 3.5
2015 3.7 3.6 3.6 3.5 3.5 3.6 3.5 3.5 3.6 3.4 3.4 3.5
2016 3.4 3.6 3.5 3.4 3.4 3.2 3.2 3.4 3.3 3.2 3.2 3.3
2017 3.2 3.1 2.9 2.9 3.2 2.9 3.0 2.9 2.9 2.8 2.8 2.8
2018 2.6 2.6 2.7 2.8 2.4 2.6 2.7 2.6 2.5 2.6 2.6 2.5
2019 2.5 2.5 2.8 2.5 2.5 2.6 2.4 2.4 2.6 2.5
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36583 -0.06403 -0.00336 0.09998 0.35474
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.624696 0.047064 119.51 <0.0000000000000002 ***
ID -0.031619 0.002051 -15.42 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1441 on 37 degrees of freedom
Multiple R-squared: 0.8653, Adjusted R-squared: 0.8617
F-statistic: 237.7 on 1 and 37 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 = 0.17949, p-value = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.003, p-value = 0.000192
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.5371, df = 1, p-value = 0.215
Box-Ljung test
data: lm_residuals
X-squared = 9.1604, df = 1, p-value = 0.002473
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.32364 -0.08642 -0.00123 0.07969 0.32469
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.3245709 0.0306336 141.17 <0.0000000000000002 ***
ID -0.0246297 0.0006412 -38.41 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1374 on 80 degrees of freedom
Multiple R-squared: 0.9486, Adjusted R-squared: 0.9479
F-statistic: 1475 on 1 and 80 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 = 0.10976, p-value = 0.7099
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0262, p-value = 0.0000006279
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1262, df = 1, p-value = 0.2886
Box-Ljung test
data: lm_residuals
X-squared = 18.077, df = 1, p-value = 0.00002121
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.96601 -0.32394 -0.04187 0.48089 0.96710
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.974284 0.132165 37.64 <0.0000000000000002 ***
ID -0.002759 0.003831 -0.72 0.474
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5011 on 57 degrees of freedom
Multiple R-squared: 0.009014, Adjusted R-squared: -0.008372
F-statistic: 0.5184 on 1 and 57 DF, p-value: 0.4744
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22034, p-value = 0.1141
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.11055, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 27.018, df = 1, p-value = 0.0000002015
Box-Ljung test
data: lm_residuals
X-squared = 51.929, df = 1, p-value = 0.0000000000005754
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33081 -0.07904 0.00013 0.07907 0.30926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.210029 0.029191 144.22 <0.0000000000000002 ***
ID -0.023858 0.000634 -37.63 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1285 on 77 degrees of freedom
Multiple R-squared: 0.9484, Adjusted R-squared: 0.9478
F-statistic: 1416 on 1 and 77 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 = 0.063291, p-value = 0.9977
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1985, p-value = 0.00005837
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.028303, df = 1, p-value = 0.8664
Box-Ljung test
data: lm_residuals
X-squared = 11.278, df = 1, p-value = 0.0007845