Analysis
[1] "景気動向指数個別系列:一致系列:所定外労働時間指数(調査産業計)(平成27年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 89.6
2000 89.3 89.1 91.6 91.5 92.1 91.8 93.1 93.4 94.3 93.7 94.5 93.3
2001 93.6 92.8 91.9 90.3 89.8 89.8 88.4 87.7 86.5 86.2 84.9 85.2
2002 85.8 86.4 87.3 89.1 90.5 89.8 89.0 90.6 90.5 91.7 92.4 92.5
2003 93.0 93.7 93.5 93.0 94.5 93.8 94.5 95.4 96.3 96.5 96.9 97.6
2004 97.6 97.6 98.4 97.5 97.9 97.8 98.2 98.6 97.9 96.7 97.9 98.2
2005 98.3 97.8 95.6 98.2 98.1 98.8 98.9 98.5 97.6 98.2 97.8 100.5
2006 100.8 100.5 100.7 101.5 101.5 102.0 101.9 101.7 101.0 101.0 102.2 102.1
2007 102.6 103.4 103.5 105.1 103.8 104.4 103.2 103.2 104.1 103.9 103.9 103.9
2008 102.9 106.3 106.3 104.1 104.0 102.4 101.7 100.1 100.0 98.5 95.0 89.8
2009 85.2 79.3 78.0 80.5 81.8 82.2 83.9 85.2 86.4 88.7 88.7 90.1
2010 92.1 92.5 93.6 95.3 94.1 93.7 93.9 94.6 93.5 93.3 94.8 93.6
2011 94.0 95.3 92.4 89.6 90.2 92.7 93.8 92.9 94.4 94.9 94.2 95.4
2012 95.5 96.1 95.8 94.9 95.8 95.2 93.7 95.0 94.0 93.2 93.2 93.8
2013 93.9 94.6 94.4 95.6 96.7 96.7 98.4 99.2 98.6 99.4 100.0 99.8
2014 101.0 100.7 102.7 101.7 101.7 100.8 101.0 100.0 100.1 100.7 99.8 101.4
2015 101.8 100.0 100.2 100.0 99.3 100.3 99.9 99.7 100.2 100.0 99.6 99.1
2016 98.4 98.4 98.8 98.5 97.9 98.7 98.5 98.2 98.4 98.3 97.8 97.7
2017 98.8 99.4 98.3 98.2 98.3 97.1 97.3 98.5 98.5 97.8 98.6 98.0
2018 95.6 97.0 96.8 96.8 99.2 97.9 97.4 96.8 96.1 98.6 97.9 95.1
2019 96.4 97.8 96.1 97.6 99.2 97.1 96.6 95.1 96.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.8706 -0.9564 0.4522 1.2294 2.7977
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.93752 0.54440 168.879 < 0.0000000000000002 ***
ID 0.08069 0.02372 3.401 0.00162 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.667 on 37 degrees of freedom
Multiple R-squared: 0.2382, Adjusted R-squared: 0.2176
F-statistic: 11.57 on 1 and 37 DF, p-value: 0.001622
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 = 0.9885
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.54693, p-value = 0.000000007334
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.4199, df = 1, p-value = 0.06442
Box-Ljung test
data: lm_residuals
X-squared = 18.608, df = 1, p-value = 0.00001605
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-5.7422 -0.7336 0.1920 1.0536 3.4748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.672006 0.381066 261.56 < 0.0000000000000002 ***
ID -0.029790 0.008074 -3.69 0.000411 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.699 on 79 degrees of freedom
Multiple R-squared: 0.147, Adjusted R-squared: 0.1362
F-statistic: 13.61 on 1 and 79 DF, p-value: 0.0004108
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 = 0.3756, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 16.127, df = 1, p-value = 0.00005924
Box-Ljung test
data: lm_residuals
X-squared = 45.575, df = 1, p-value = 0.00000000001469
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-13.5046 -1.2231 0.6661 1.9542 13.0898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 90.85073 1.38490 65.601 <0.0000000000000002 ***
ID 0.05944 0.04015 1.481 0.144
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.251 on 57 degrees of freedom
Multiple R-squared: 0.03703, Adjusted R-squared: 0.02014
F-statistic: 2.192 on 1 and 57 DF, p-value: 0.1442
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.28814, p-value = 0.01452
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.11639, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 26.438, df = 1, p-value = 0.0000002721
Box-Ljung test
data: lm_residuals
X-squared = 48.854, df = 1, p-value = 0.000000000002758
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-4.7862 -0.6962 0.0744 0.7434 2.8212
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.432334 0.298191 336.806 < 0.0000000000000002 ***
ID -0.046129 0.006559 -7.033 0.00000000076 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.304 on 76 degrees of freedom
Multiple R-squared: 0.3943, Adjusted R-squared: 0.3863
F-statistic: 49.47 on 1 and 76 DF, p-value: 0.0000000007597
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11538, p-value = 0.6802
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.64739, p-value = 0.0000000000008209
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 8.9208, df = 1, p-value = 0.002819
Box-Ljung test
data: lm_residuals
X-squared = 27.733, df = 1, p-value = 0.0000001392