Analysis
[1] "景気動向指数個別系列:先行系列:寄与度:新規求人数(除学卒):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 0.21
2000 0.55 -0.17 0.17 0.31 0.15 0.37 0.32 0.02 0.03 0.11 0.17 -0.11
2001 -0.41 0.01 -0.30 0.08 -0.16 -0.12 0.00 -0.33 -0.14 -0.27 -0.06 -0.09
2002 0.13 0.19 0.17 0.03 0.22 -0.08 -0.06 0.17 0.05 0.05 -0.14 0.16
2003 0.15 -0.01 0.14 -0.14 0.01 0.09 0.27 0.06 0.15 0.09 0.08 0.24
2004 -0.28 -0.41 0.25 0.14 -0.10 0.13 -0.05 -0.04 0.13 0.23 0.33 -0.42
2005 -0.13 -0.09 0.19 -0.03 0.04 0.00 -0.10 -0.03 -0.06 -0.13 0.33 -0.28
2006 0.11 0.12 -0.39 0.13 0.08 -0.19 0.00 -0.14 -0.17 -0.50 0.29 -0.07
2007 -0.56 0.18 -0.05 -0.20 -0.27 -0.12 -0.20 -0.24 -0.37 -0.20 -0.40 -0.28
2008 0.06 0.01 -0.67 0.23 -0.02 -0.32 -0.25 -0.03 -0.40 -0.18 -0.15 0.13
2009 -0.62 -0.49 0.00 -0.05 -0.18 0.36 0.13 0.03 0.31 0.05 0.08 0.20
2010 0.00 -0.24 0.55 0.15 0.39 0.34 0.14 0.35 0.22 0.36 0.10 -0.03
2011 0.26 0.19 -0.60 0.43 0.25 0.18 0.47 0.10 0.10 0.31 0.16 0.06
2012 0.08 0.08 0.12 0.15 0.18 -0.03 0.02 0.16 -0.10 -0.03 0.05 0.03
2013 0.06 0.34 -0.02 -0.17 0.19 0.17 0.00 -0.01 0.18 -0.01 -0.20 0.14
2014 0.32 -0.48 -0.13 0.18 -0.33 -0.04 -0.09 -0.15 -0.04 -0.16 0.05 -0.01
2015 0.24 -0.50 -0.32 0.25 -0.01 -0.03 0.11 -0.06 -0.05 0.23 -0.12 -0.14
2016 0.10 0.05 -0.53 0.31 0.10 -0.17 0.07 -0.12 0.05 -0.06 -0.06 0.18
2017 -0.06 -0.05 -0.15 0.02 0.05 0.09 -0.08 0.16 -0.13 -0.08 0.09 0.22
2018 -0.54 0.17 0.03 -0.02 -0.17 0.00 -0.14 -0.05 -0.03 -0.03 -0.08 -0.04
2019 0.28 0.04 -0.51 0.13 0.34 -0.46 -0.22 0.04 -0.47
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.74251 -0.06113 -0.02113 0.09887 0.36471
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.206680 0.065426 3.159 0.00315 **
ID -0.003565 0.002851 -1.250 0.21901
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2004 on 37 degrees of freedom
Multiple R-squared: 0.04054, Adjusted R-squared: 0.01461
F-statistic: 1.563 on 1 and 37 DF, p-value: 0.219
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 = 2.3026, p-value = 0.7842
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.2177, df = 1, p-value = 0.2698
Box-Ljung test
data: lm_residuals
X-squared = 1.0728, df = 1, p-value = 0.3003
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.50338 -0.08771 -0.00037 0.12889 0.40967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0175556 0.0457850 0.383 0.702
ID -0.0011328 0.0009701 -1.168 0.246
Residual standard error: 0.2041 on 79 degrees of freedom
Multiple R-squared: 0.01697, Adjusted R-squared: 0.004525
F-statistic: 1.364 on 1 and 79 DF, p-value: 0.2464
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1358, p-value = 0.4462
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.452, p-value = 0.9746
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.4581, df = 1, p-value = 0.4985
Box-Ljung test
data: lm_residuals
X-squared = 5.2556, df = 1, p-value = 0.02188
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.68897 -0.13385 0.01835 0.15014 0.51469
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.067539 0.061302 -1.102 0.2752
ID 0.004472 0.001777 2.516 0.0147 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2324 on 57 degrees of freedom
Multiple R-squared: 0.09998, Adjusted R-squared: 0.08419
F-statistic: 6.332 on 1 and 57 DF, p-value: 0.0147
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.7461, p-value = 0.1311
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.7274, df = 1, p-value = 0.1887
Box-Ljung test
data: lm_residuals
X-squared = 0.87921, df = 1, p-value = 0.3484
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.49789 -0.08973 0.00593 0.13388 0.40208
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0037229 0.0467504 -0.080 0.937
ID -0.0007886 0.0010282 -0.767 0.446
Residual standard error: 0.2045 on 76 degrees of freedom
Multiple R-squared: 0.00768, Adjusted R-squared: -0.005377
F-statistic: 0.5882 on 1 and 76 DF, p-value: 0.4455
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14103, p-value = 0.4221
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.4681, p-value = 0.9765
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.50366, df = 1, p-value = 0.4779
Box-Ljung test
data: lm_residuals
X-squared = 5.6544, df = 1, p-value = 0.01741