Analysis
[1] "景気動向指数個別系列:一致系列:生産指数(鉱工業)(平成27年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 104.4
2000 104.4 104.1 105.9 107.3 106.9 108.5 107.9 109.4 107.2 108.9 109.3 110.7
2001 106.0 107.2 105.4 104.3 102.2 101.0 99.4 98.2 96.2 96.1 94.5 95.5
2002 94.8 96.3 97.1 96.5 100.7 99.6 100.3 100.6 101.3 101.4 101.0 100.9
2003 101.4 101.0 101.6 100.3 101.7 100.9 101.6 100.3 103.2 105.0 104.7 104.6
2004 106.3 106.1 105.6 107.4 107.4 107.7 109.0 107.8 108.0 106.4 107.4 106.0
2005 108.4 108.2 108.6 109.1 108.4 108.7 107.8 107.9 108.9 108.4 110.1 110.3
2006 110.8 110.7 111.3 113.5 111.9 113.3 113.7 114.1 114.1 115.0 115.4 115.7
2007 114.4 115.1 115.1 114.6 115.9 116.0 116.1 119.1 117.2 119.4 117.7 118.5
2008 119.1 119.4 118.3 117.6 118.2 114.9 114.7 110.7 112.0 109.3 102.0 93.6
2009 85.3 78.0 79.0 82.5 85.5 87.1 88.3 89.6 92.6 95.0 97.0 97.8
2010 100.3 100.7 100.9 102.0 101.8 101.0 102.1 102.5 104.1 101.2 102.8 103.4
2011 103.9 104.5 87.3 89.2 95.3 99.3 100.5 102.2 101.3 103.1 100.9 102.9
2012 103.3 103.1 102.9 102.4 100.6 99.8 99.3 97.8 95.7 96.0 95.1 96.4
2013 94.8 96.5 97.7 97.7 99.3 98.2 99.8 100.0 101.0 101.2 101.8 101.8
2014 103.8 102.7 104.2 99.6 101.9 100.3 100.1 99.5 100.7 100.4 100.4 99.9
2015 102.9 99.8 99.3 99.5 99.5 100.4 100.3 98.6 100.6 100.7 99.9 98.5
2016 100.1 99.2 99.7 99.3 98.5 99.2 99.8 100.5 100.7 101.0 102.0 102.0
2017 100.9 101.6 101.5 104.1 102.3 103.3 102.5 104.0 103.0 103.3 104.2 105.8
2018 101.4 104.0 105.1 104.5 104.8 103.7 103.8 103.6 103.5 105.6 104.6 104.7
2019 102.1 102.8 102.2 102.8 104.9 101.4 102.7 101.5 103.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-12.655 -2.119 1.063 2.809 4.509
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.61120 1.27380 78.985 <0.0000000000000002 ***
ID -0.03646 0.05551 -0.657 0.515
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.901 on 37 degrees of freedom
Multiple R-squared: 0.01153, Adjusted R-squared: -0.01519
F-statistic: 0.4314 on 1 and 37 DF, p-value: 0.5154
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 = 0.73299, p-value = 0.000001258
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.033081, df = 1, p-value = 0.8557
Box-Ljung test
data: lm_residuals
X-squared = 15.098, df = 1, p-value = 0.0001021
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-4.1147 -1.1037 -0.2527 1.2327 4.4098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.852160 0.372485 265.385 < 0.0000000000000002 ***
ID 0.062534 0.007892 7.924 0.0000000000123 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.661 on 79 degrees of freedom
Multiple R-squared: 0.4428, Adjusted R-squared: 0.4358
F-statistic: 62.79 on 1 and 79 DF, p-value: 0.00000000001227
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.19753, p-value = 0.08471
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.78625, p-value = 0.0000000002108
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.4648, df = 1, p-value = 0.06269
Box-Ljung test
data: lm_residuals
X-squared = 27.002, df = 1, p-value = 0.0000002033
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-21.047 -3.357 1.848 4.178 19.030
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.18311 2.15502 46.024 <0.0000000000000002 ***
ID -0.01362 0.06247 -0.218 0.828
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.172 on 57 degrees of freedom
Multiple R-squared: 0.0008329, Adjusted R-squared: -0.0167
F-statistic: 0.04752 on 1 and 57 DF, p-value: 0.8282
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23729, p-value = 0.07193
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.19537, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 22.651, df = 1, p-value = 0.000001943
Box-Ljung test
data: lm_residuals
X-squared = 45.327, df = 1, p-value = 0.00000000001667
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-3.0300 -1.1754 -0.2542 1.2988 4.0828
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.465135 0.361099 275.451 < 0.0000000000000002 ***
ID 0.054339 0.007942 6.842 0.00000000174 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.579 on 76 degrees of freedom
Multiple R-squared: 0.3812, Adjusted R-squared: 0.373
F-statistic: 46.81 on 1 and 76 DF, p-value: 0.000000001741
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 = 0.316
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.88283, p-value = 0.00000001459
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.75896, df = 1, p-value = 0.3837
Box-Ljung test
data: lm_residuals
X-squared = 24.441, df = 1, p-value = 0.000000766
