Analysis
[1] "景気動向指数個別系列:遅行系列:常用雇用指数(調査産業計)(前年同月比)(%):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.8
2000 -1.0 -0.9 -0.6 -0.8 -0.7 -0.6 -0.6 -0.7 -1.0 -0.7 -0.6 -0.7
2001 -0.8 -0.8 -0.9 -0.9 -0.8 -1.0 -0.9 -1.0 -1.0 -1.0 -1.2 -1.0
2002 -0.9 -0.9 -1.1 -1.2 -1.4 -1.4 -1.6 -1.7 -1.4 -1.4 -1.4 -1.4
2003 -1.5 -1.4 -1.4 -1.4 -1.2 -0.9 -0.8 -0.5 -0.7 -0.7 -0.5 -0.6
2004 -0.3 -0.2 0.2 0.5 0.6 0.5 0.6 0.7 0.8 0.8 0.8 0.9
2005 1.0 0.7 0.7 0.9 1.1 1.0 0.9 0.8 0.6 0.8 0.7 0.7
2006 0.5 0.6 0.7 0.9 0.7 0.9 1.1 1.1 1.4 1.3 1.4 1.5
2007 1.8 1.9 1.9 2.2 2.4 2.7 2.8 2.9 3.0 3.2 3.5 3.4
2008 3.3 3.5 3.5 3.5 3.6 3.2 3.2 3.1 3.1 3.0 2.7 2.8
2009 2.6 2.3 1.9 1.6 0.8 0.9 0.6 0.6 0.4 0.3 0.1 0.1
2010 -0.1 0.2 0.1 0.0 0.3 0.2 0.4 0.5 0.6 0.5 0.6 0.5
2011 0.8 0.7 0.9 0.6 0.6 0.6 0.6 0.5 0.5 0.2 0.5 0.3
2012 0.4 0.6 0.4 0.4 0.6 0.5 0.3 0.2 0.1 0.4 0.0 0.4
2013 -0.1 -0.2 -0.1 0.1 0.3 0.5 0.5 0.6 0.6 0.7 0.9 0.8
2014 1.0 0.8 0.9 1.0 0.9 0.9 1.0 0.9 0.7 0.7 0.5 0.7
2015 0.9 1.1 0.8 1.2 0.9 1.0 0.9 0.9 1.0 1.1 1.2 1.3
2016 1.1 1.0 1.1 0.9 0.8 0.9 0.8 0.9 1.0 0.9 0.9 0.9
2017 1.0 1.0 1.1 1.4 1.7 1.4 1.6 1.5 1.5 1.6 1.7 1.5
2018 0.6 0.9 0.7 0.5 0.6 0.5 0.2 0.3 0.1 0.1 0.1 0.1
2019 1.3 1.2 1.1 1.1 0.8 0.9 1.2 1.2 1.5
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.45645 -0.15040 -0.02224 0.16921 0.51197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.326451 0.075173 4.343 0.000105 ***
ID 0.003421 0.003276 1.044 0.303075
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2302 on 37 degrees of freedom
Multiple R-squared: 0.02864, Adjusted R-squared: 0.002383
F-statistic: 1.091 on 1 and 37 DF, p-value: 0.3031
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 = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.73924, p-value = 0.000001452
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.33666, df = 1, p-value = 0.5618
Box-Ljung test
data: lm_residuals
X-squared = 16.661, df = 1, p-value = 0.00004469
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.92911 -0.16079 0.08209 0.24513 0.77121
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.648951 0.091157 7.119 0.000000000442 ***
ID 0.005280 0.001931 2.734 0.00772 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4064 on 79 degrees of freedom
Multiple R-squared: 0.08643, Adjusted R-squared: 0.07486
F-statistic: 7.474 on 1 and 79 DF, p-value: 0.007724
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22222, p-value = 0.03633
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.33454, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.3968, df = 1, p-value = 0.03601
Box-Ljung test
data: lm_residuals
X-squared = 54.332, df = 1, p-value = 0.0000000000001693
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.3587 -0.5288 0.1025 0.3755 1.4800
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.163063 0.188085 11.500 < 0.0000000000000002 ***
ID -0.043063 0.005452 -7.898 0.000000000102 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7132 on 57 degrees of freedom
Multiple R-squared: 0.5225, Adjusted R-squared: 0.5142
F-statistic: 62.38 on 1 and 57 DF, p-value: 0.0000000001015
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 = 0.10206, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 28.248, df = 1, p-value = 0.0000001068
Box-Ljung test
data: lm_residuals
X-squared = 51.37, df = 1, p-value = 0.0000000000007649
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.88774 -0.14981 0.04024 0.19164 0.76629
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.791508 0.086390 9.162 0.0000000000000654 ***
ID 0.002844 0.001900 1.497 0.139
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3778 on 76 degrees of freedom
Multiple R-squared: 0.02863, Adjusted R-squared: 0.01585
F-statistic: 2.24 on 1 and 76 DF, p-value: 0.1386
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10256, p-value = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.39752, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.84, df = 1, p-value = 0.0005796
Box-Ljung test
data: lm_residuals
X-squared = 47.812, df = 1, p-value = 0.000000000004691