Analysis
[1] "景気動向指数個別系列:遅行系列:第3次産業活動指数(対事業所サービス業)(平成22年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 97.4
2000 97.4 95.2 98.6 97.9 97.7 98.2 98.8 100.0 100.6 101.5 101.3 101.4
2001 101.3 102.4 102.1 104.5 103.0 102.0 101.8 102.3 102.2 102.2 103.4 103.3
2002 102.9 102.6 103.4 102.9 103.1 103.1 103.5 103.2 102.6 102.0 101.9 102.6
2003 104.1 104.7 108.5 104.0 104.7 104.8 104.9 105.0 105.9 106.3 105.1 104.9
2004 106.3 104.5 103.3 105.1 105.6 105.8 106.8 106.7 106.6 107.3 109.2 108.9
2005 110.2 109.6 111.3 111.2 108.7 111.6 110.0 112.2 112.6 110.5 113.0 112.9
2006 114.3 114.1 113.8 113.6 115.2 115.4 115.0 116.0 116.0 116.2 115.7 116.6
2007 116.2 116.7 116.7 117.9 117.8 118.0 119.4 116.9 116.2 117.0 118.6 118.0
2008 117.4 116.9 116.6 116.4 116.1 114.3 112.7 112.1 111.3 111.8 110.6 110.9
2009 109.0 107.2 108.0 105.2 103.7 104.5 104.0 107.3 107.9 103.2 102.6 99.8
2010 102.2 101.3 100.9 98.9 100.2 100.0 102.2 99.1 98.7 99.1 100.1 98.5
2011 100.1 101.0 98.5 99.9 99.1 100.2 99.7 100.1 101.8 100.8 100.4 101.8
2012 100.0 101.5 103.4 102.8 103.3 101.4 100.8 101.1 100.9 101.3 101.4 102.6
2013 99.6 99.2 102.0 102.2 105.5 103.0 105.1 106.5 103.3 103.9 102.3 101.6
2014 102.0 102.0 102.4 103.0 102.2 102.3 103.4 100.5 100.9 100.9 100.7 101.9
2015 102.0 102.6 102.1 103.1 102.1 102.5 101.8 102.9 102.3 101.2 102.4 101.4
2016 103.8 102.9 104.0 106.2 104.2 104.6 105.1 104.5 106.0 104.3 105.1 105.8
2017 107.0 107.0 107.6 108.2 106.7 107.7 106.8 106.8 105.5 107.7 106.9 108.0
2018 106.7 107.6 108.8 108.9 108.4 109.2 108.8 108.4 109.3 109.8 110.8 111.4
2019 111.5 111.1 112.7 113.7 112.3 111.2 111.2 110.1 111.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-2.2261 -0.8177 -0.2209 0.8449 2.9928
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.17665 0.43721 229.126 <0.0000000000000002 ***
ID 0.03053 0.01905 1.602 0.118
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.339 on 37 degrees of freedom
Multiple R-squared: 0.06489, Adjusted R-squared: 0.03961
F-statistic: 2.567 on 1 and 37 DF, p-value: 0.1176
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 = 1.1205, p-value = 0.0009629
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.5864, df = 1, p-value = 0.03223
Box-Ljung test
data: lm_residuals
X-squared = 5.4778, df = 1, p-value = 0.01926
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.3461 -1.2111 -0.0521 0.8679 5.4779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.958148 0.383627 260.56 <0.0000000000000002 ***
ID 0.132999 0.008128 16.36 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.71 on 79 degrees of freedom
Multiple R-squared: 0.7722, Adjusted R-squared: 0.7693
F-statistic: 267.7 on 1 and 79 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 = 0.08642, p-value = 0.9254
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.51825, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 6.0736, df = 1, p-value = 0.01372
Box-Ljung test
data: lm_residuals
X-squared = 46.003, df = 1, p-value = 0.0000000000118
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-5.3970 -2.6254 -0.1705 2.4283 7.5525
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 108.73226 0.83754 129.824 < 0.0000000000000002 ***
ID -0.18480 0.02428 -7.612 0.000000000304 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.176 on 57 degrees of freedom
Multiple R-squared: 0.5041, Adjusted R-squared: 0.4954
F-statistic: 57.94 on 1 and 57 DF, p-value: 0.0000000003042
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 = 0.25817, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.943, df = 1, p-value = 0.004827
Box-Ljung test
data: lm_residuals
X-squared = 40.328, df = 1, p-value = 0.0000000002147
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.3440 -1.2376 -0.0321 0.8651 5.4821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.352481 0.395262 253.89 <0.0000000000000002 ***
ID 0.133076 0.008694 15.31 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.729 on 76 degrees of freedom
Multiple R-squared: 0.7551, Adjusted R-squared: 0.7519
F-statistic: 234.3 on 1 and 76 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 = 0.10256, p-value = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.49477, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.5949, df = 1, p-value = 0.005853
Box-Ljung test
data: lm_residuals
X-squared = 45.059, df = 1, p-value = 0.00000000001912
