Analysis
[1] "景気動向指数個別系列:一致系列:投資財出荷指数(除輸送機械)(平成27年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 120.3
2000 122.1 122.1 124.3 125.4 123.5 128.1 126.6 132.3 127.9 130.4 130.0 132.7
2001 128.5 130.1 125.4 125.0 121.7 120.7 116.4 112.7 110.8 109.2 109.0 110.0
2002 107.8 107.3 108.2 103.0 110.5 105.8 105.3 109.0 109.8 108.0 107.6 105.8
2003 106.2 108.2 108.1 102.1 105.1 105.7 105.1 105.0 108.9 111.8 108.6 110.1
2004 114.2 113.0 113.1 114.6 116.2 117.1 118.6 117.1 117.4 115.4 116.2 117.1
2005 117.5 114.9 117.5 119.5 116.8 118.0 116.9 120.6 118.5 119.2 122.1 123.5
2006 120.5 120.1 120.5 123.9 122.9 126.2 123.7 122.4 123.1 125.2 124.3 124.0
2007 124.9 126.3 120.1 123.1 124.9 124.4 125.6 124.8 122.9 123.1 121.9 122.9
2008 120.6 120.5 119.9 118.5 123.3 116.5 113.9 111.5 111.3 110.0 103.1 98.2
2009 94.6 88.3 87.3 79.6 78.7 78.0 76.7 80.4 81.7 81.8 82.2 84.0
2010 86.6 90.2 90.8 91.8 88.8 92.2 93.1 93.2 95.1 96.7 97.3 96.8
2011 97.0 101.0 89.9 94.4 97.0 98.5 98.0 98.7 93.7 98.8 100.1 100.3
2012 99.8 97.6 98.6 97.2 100.0 96.7 96.1 94.2 93.4 91.6 90.7 94.4
2013 91.7 93.4 97.9 95.7 96.3 95.7 97.2 98.7 98.5 100.5 101.1 101.8
2014 108.3 103.9 109.0 100.7 99.8 100.6 101.7 100.0 102.2 101.7 101.2 100.4
2015 107.7 98.9 99.9 100.1 100.0 101.0 101.2 99.1 100.0 99.4 97.7 96.1
2016 98.5 97.8 97.7 98.3 97.2 97.8 98.7 99.0 99.4 99.7 100.9 100.1
2017 99.4 99.0 97.2 100.9 102.4 102.9 100.7 105.8 102.2 103.5 105.2 107.0
2018 103.4 103.3 105.0 107.5 104.5 103.4 103.6 104.1 103.6 107.8 105.0 104.5
2019 98.2 101.5 99.6 101.0 104.2 99.7 100.8 101.6 108.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-7.995 -3.180 0.648 3.403 7.415
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.44858 1.33678 66.914 < 0.0000000000000002 ***
ID 0.24334 0.05825 4.178 0.000172 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.094 on 37 degrees of freedom
Multiple R-squared: 0.3205, Adjusted R-squared: 0.3021
F-statistic: 17.45 on 1 and 37 DF, p-value: 0.0001723
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 = 0.51579, p-value = 0.000000002515
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.051917, df = 1, p-value = 0.8198
Box-Ljung test
data: lm_residuals
X-squared = 19.188, df = 1, p-value = 0.00001184
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-6.7270 -2.2416 -0.1771 1.4083 9.6729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.36275 0.68903 142.756 < 0.0000000000000002 ***
ID 0.06429 0.01460 4.404 0.000033 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.072 on 79 degrees of freedom
Multiple R-squared: 0.1971, Adjusted R-squared: 0.187
F-statistic: 19.4 on 1 and 79 DF, p-value: 0.00003299
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12346, p-value = 0.5705
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.83427, p-value = 0.000000001402
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.9673, df = 1, p-value = 0.1607
Box-Ljung test
data: lm_residuals
X-squared = 24.337, df = 1, p-value = 0.0000008087
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.6937 -4.5086 0.2414 4.2250 27.3712
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.96704 2.47153 38.829 <0.0000000000000002 ***
ID -0.03822 0.07165 -0.534 0.596
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.372 on 57 degrees of freedom
Multiple R-squared: 0.004969, Adjusted R-squared: -0.01249
F-statistic: 0.2846 on 1 and 57 DF, p-value: 0.5958
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15254, p-value = 0.5021
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.123, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 24.742, df = 1, p-value = 0.0000006553
Box-Ljung test
data: lm_residuals
X-squared = 46.099, df = 1, p-value = 0.00000000001124
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-4.8213 -2.2211 -0.2779 1.4593 9.1573
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.22631 0.67651 146.673 < 0.0000000000000002 ***
ID 0.05136 0.01488 3.452 0.000912 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.959 on 76 degrees of freedom
Multiple R-squared: 0.1355, Adjusted R-squared: 0.1242
F-statistic: 11.92 on 1 and 76 DF, p-value: 0.0009124
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.076923, p-value = 0.9766
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.89386, p-value = 0.00000002109
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.7244, df = 1, p-value = 0.3947
Box-Ljung test
data: lm_residuals
X-squared = 22.328, df = 1, p-value = 0.000002298