Analysis
[1] "景気動向指数個別系列:一致系列:鉱工業用生産財出荷指数(平成27年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 91.5
2000 91.1 91.6 92.9 95.3 94.9 95.9 95.9 96.9 95.1 96.3 96.4 96.6
2001 93.0 93.5 91.5 89.7 87.7 86.9 85.7 84.8 83.9 82.8 82.2 83.3
2002 83.7 85.8 87.4 89.0 92.5 90.6 91.0 92.1 92.9 93.1 92.9 92.1
2003 94.9 95.0 94.8 94.0 95.1 94.4 95.6 95.5 96.9 99.6 98.9 100.1
2004 102.2 101.3 100.0 101.9 103.0 102.9 103.3 102.3 102.3 101.5 102.5 100.5
2005 102.2 101.9 102.8 103.5 102.6 104.4 103.4 103.7 105.5 105.7 106.1 106.2
2006 106.7 106.6 107.1 108.0 106.5 108.7 109.5 111.0 109.9 110.0 110.9 111.4
2007 110.7 111.0 111.5 111.5 112.3 112.6 112.8 115.3 114.6 117.3 115.6 117.2
2008 116.6 116.9 116.2 113.5 113.6 110.4 110.5 105.9 106.3 102.7 93.3 83.8
2009 74.2 69.0 71.6 76.0 79.8 83.1 85.2 87.0 91.0 93.5 95.4 98.0
2010 102.8 102.6 103.0 102.0 102.4 99.7 100.0 99.8 99.4 98.1 101.3 102.4
2011 104.3 105.8 90.7 87.7 89.4 94.1 95.4 97.6 98.2 99.6 96.7 100.3
2012 99.7 101.1 102.4 100.0 98.3 97.0 95.6 95.1 93.4 93.8 93.3 95.7
2013 95.6 96.8 98.7 98.9 100.3 99.3 101.7 101.6 101.7 101.8 102.7 102.2
2014 103.3 101.9 102.2 100.3 101.7 99.7 100.4 99.3 101.3 101.2 100.9 100.9
2015 102.7 100.8 99.6 100.0 99.8 100.3 99.5 98.2 101.4 101.0 99.1 97.8
2016 99.3 98.9 98.5 98.9 98.2 99.3 99.9 100.4 101.1 101.8 102.8 103.1
2017 102.2 102.4 103.0 104.2 101.7 104.1 103.5 105.1 103.5 103.2 105.3 106.0
2018 101.8 104.4 104.9 105.5 105.2 105.1 104.4 104.2 103.4 107.4 104.4 104.8
2019 102.4 101.8 102.2 102.3 104.4 101.5 103.5 101.1 101.6
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.5069 -2.1923 0.3974 2.8315 7.3639
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.38421 1.30562 76.886 <0.0000000000000002 ***
ID -0.11460 0.05689 -2.014 0.0513 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.999 on 37 degrees of freedom
Multiple R-squared: 0.09882, Adjusted R-squared: 0.07446
F-statistic: 4.057 on 1 and 37 DF, p-value: 0.05129
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 = 0.64264, p-value = 0.0000001304
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.75197, df = 1, p-value = 0.3859
Box-Ljung test
data: lm_residuals
X-squared = 17.231, df = 1, p-value = 0.00003311
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.7765 -1.2835 0.0819 1.4877 4.0239
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.318488 0.413534 240.170 < 0.0000000000000002 ***
ID 0.057965 0.008762 6.616 0.00000000402 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.844 on 79 degrees of freedom
Multiple R-squared: 0.3565, Adjusted R-squared: 0.3484
F-statistic: 43.77 on 1 and 79 DF, p-value: 0.000000004016
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.098765, p-value = 0.8277
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.64301, p-value = 0.0000000000002565
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0012336, df = 1, p-value = 0.972
Box-Ljung test
data: lm_residuals
X-squared = 34.539, df = 1, p-value = 0.000000004177
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-25.647 -3.846 1.023 5.337 19.532
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 94.00362 2.40689 39.056 <0.0000000000000002 ***
ID 0.06434 0.06977 0.922 0.36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.127 on 57 degrees of freedom
Multiple R-squared: 0.0147, Adjusted R-squared: -0.002586
F-statistic: 0.8504 on 1 and 57 DF, p-value: 0.3603
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.17062, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.77, df = 1, p-value = 0.000005178
Box-Ljung test
data: lm_residuals
X-squared = 47.446, df = 1, p-value = 0.000000000005654
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.7460 -1.3036 0.0081 1.4675 4.1366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.879154 0.409566 243.866 < 0.0000000000000002 ***
ID 0.050512 0.009008 5.607 0.000000317 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.791 on 76 degrees of freedom
Multiple R-squared: 0.2926, Adjusted R-squared: 0.2833
F-statistic: 31.44 on 1 and 76 DF, p-value: 0.0000003167
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 = 0.68868, p-value = 0.000000000006239
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1403, df = 1, p-value = 0.2856
Box-Ljung test
data: lm_residuals
X-squared = 33.546, df = 1, p-value = 0.00000000696