Analysis
[1] "景気動向指数個別系列:遅行系列:最終需要財在庫指数(平成27年=100):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 127.2
2000 128.3 127.4 129.1 128.6 127.8 127.7 126.8 127.1 124.7 126.2 126.0 125.1
2001 124.8 124.7 124.4 126.0 127.3 127.4 126.2 127.3 126.3 125.1 123.8 123.5
2002 123.0 122.0 119.9 118.2 118.1 115.1 115.4 113.1 113.3 115.5 111.7 113.4
2003 116.3 112.2 111.5 110.3 110.5 108.8 112.6 109.2 109.8 109.9 109.4 107.4
2004 107.1 107.1 108.6 112.1 110.5 110.8 109.3 110.7 114.0 109.8 110.0 106.9
2005 109.8 111.6 113.0 111.0 110.8 112.2 113.1 113.1 114.2 110.1 111.7 112.8
2006 113.3 113.9 113.9 116.3 113.7 114.7 113.7 113.1 112.5 114.7 115.2 117.3
2007 115.3 114.2 113.0 113.3 114.6 113.5 114.4 115.4 116.2 118.3 119.9 119.2
2008 117.8 119.2 118.1 113.6 115.2 115.8 118.0 115.0 116.9 117.7 116.2 115.0
2009 117.3 109.5 106.2 103.3 101.0 99.3 99.5 97.8 97.5 95.3 95.6 94.3
2010 95.5 95.7 94.2 95.5 95.4 95.0 93.7 92.5 93.2 91.8 93.6 95.7
2011 99.1 96.7 83.0 85.2 93.2 92.7 92.7 96.1 95.6 96.2 95.6 93.6
2012 96.5 98.5 104.0 107.3 104.8 104.8 106.8 107.9 108.3 109.1 109.5 107.5
2013 103.2 101.1 99.3 98.2 97.4 94.1 96.6 95.1 96.6 97.4 95.2 96.4
2014 99.0 98.2 93.2 95.2 100.7 104.3 104.7 105.8 105.9 105.3 105.7 105.8
2015 105.4 104.3 100.6 100.2 98.4 98.6 99.1 98.2 98.0 98.1 99.8 99.6
2016 98.6 97.2 100.7 99.5 100.0 101.0 100.0 101.7 102.3 99.7 99.7 100.4
2017 100.6 101.4 103.5 104.6 103.9 102.4 100.4 102.6 103.3 105.5 106.0 106.4
2018 104.3 104.9 107.6 105.1 105.8 103.6 103.3 103.5 101.2 102.2 102.9 104.7
2019 105.2 104.9 106.3 105.4 106.7 105.4 105.4 105.6 104.6
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-13.715 -3.097 1.726 3.637 5.616
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.83414 1.54701 58.070 < 0.0000000000000002 ***
ID 0.38227 0.06741 5.671 0.00000175 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.738 on 37 degrees of freedom
Multiple R-squared: 0.465, Adjusted R-squared: 0.4505
F-statistic: 32.16 on 1 and 37 DF, p-value: 0.000001753
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23077, p-value = 0.2523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.45442, p-value = 0.0000000002392
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.092307, df = 1, p-value = 0.7613
Box-Ljung test
data: lm_residuals
X-squared = 23.837, df = 1, p-value = 0.000001048
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-6.0539 -1.9127 -0.3794 1.2913 6.0902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.86407 0.61523 159.068 < 0.0000000000000002 ***
ID 0.09266 0.01304 7.108 0.000000000464 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.743 on 79 degrees of freedom
Multiple R-squared: 0.3901, Adjusted R-squared: 0.3824
F-statistic: 50.53 on 1 and 79 DF, p-value: 0.0000000004638
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 = 0.39854, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 16.106, df = 1, p-value = 0.0000599
Box-Ljung test
data: lm_residuals
X-squared = 50.749, df = 1, p-value = 0.000000000001049
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.345 -6.792 -3.049 8.974 12.887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.82157 2.17751 48.598 <0.0000000000000002 ***
ID -0.15648 0.06312 -2.479 0.0162 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.257 on 57 degrees of freedom
Multiple R-squared: 0.09732, Adjusted R-squared: 0.08149
F-statistic: 6.145 on 1 and 57 DF, p-value: 0.01616
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.18644, p-value = 0.2582
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.13572, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.0553, df = 1, p-value = 0.3043
Box-Ljung test
data: lm_residuals
X-squared = 52.463, df = 1, p-value = 0.0000000000004384
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-5.6637 -1.7869 -0.4613 1.1985 6.4241
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.63437 0.61583 158.540 < 0.0000000000000002 ***
ID 0.10244 0.01354 7.563 0.0000000000752 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.693 on 76 degrees of freedom
Multiple R-squared: 0.4294, Adjusted R-squared: 0.4219
F-statistic: 57.2 on 1 and 76 DF, p-value: 0.00000000007522
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16667, p-value = 0.2297
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.41192, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 12.918, df = 1, p-value = 0.0003254
Box-Ljung test
data: lm_residuals
X-squared = 50.947, df = 1, p-value = 0.0000000000009487
