Analysis
[1] "景気動向指数個別系列:先行系列:寄与度:新設住宅着工床面積:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 -0.01
2000 0.66 -0.47 0.04 0.03 -0.12 0.23 -0.23 0.05 0.10 -0.11 0.13 0.52
2001 -0.78 -0.10 0.25 -0.26 0.13 -0.16 0.37 0.07 -0.16 -0.13 0.27 -0.23
2002 0.17 0.01 -0.15 0.12 0.32 -0.49 -0.11 0.05 -0.07 0.26 -0.13 -0.07
2003 0.09 0.07 -0.15 0.31 -0.15 0.63 -0.52 -0.22 0.30 0.05 -0.16 0.38
2004 -0.09 -0.11 0.16 -0.30 0.16 0.12 0.13 -0.18 0.37 -0.46 -0.28 0.26
2005 0.51 -0.69 0.18 -0.25 0.44 -0.09 0.34 -0.19 0.03 0.08 0.02 -0.59
2006 0.32 0.38 -0.44 0.44 -0.01 -0.17 -0.41 0.45 0.14 -0.09 0.17 -0.20
2007 -0.36 -0.03 0.32 0.00 -0.54 0.52 -0.71 -0.77 0.49 0.75 0.35 0.26
2008 0.49 -0.02 -0.06 0.20 -0.28 -0.07 -0.06 0.03 0.30 -0.71 -0.39 -0.04
2009 -0.21 -0.07 -0.09 -0.14 0.08 -0.06 -0.02 -0.32 0.25 0.25 0.14 0.09
2010 0.14 -0.34 0.45 -0.11 -0.21 0.22 -0.06 0.27 0.23 -0.24 0.11 0.24
2011 -0.17 -0.11 -0.10 -0.04 0.04 0.18 0.80 -0.13 -0.67 0.05 0.41 -0.14
2012 0.13 0.39 -0.23 0.13 0.08 -0.34 0.23 -0.02 -0.18 0.39 -0.21 -0.08
2013 0.07 0.16 0.02 0.08 0.46 -0.24 0.07 -0.06 0.23 -0.03 0.06 0.05
2014 -0.34 -0.65 -0.23 -0.03 -0.44 0.15 -0.21 0.07 0.03 0.07 -0.17 0.21
2015 -0.18 0.12 0.03 -0.07 -0.09 0.57 -0.55 0.06 -0.16 -0.15 0.20 -0.11
2016 -0.13 0.54 0.04 -0.17 0.14 -0.11 0.05 -0.11 0.06 0.00 -0.15 -0.02
2017 0.40 -0.38 0.07 0.12 -0.12 0.11 -0.24 -0.12 0.08 -0.07 0.10 -0.08
2018 -0.39 0.22 -0.02 0.45 0.00 -0.40 0.23 0.05 -0.03 0.16 0.03 0.12
2019 -0.42 0.41 0.26 -0.40 -0.15 0.16 -0.04 -0.07 -0.07
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.70164 -0.17262 0.01041 0.15998 0.76380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.086437 0.088697 0.975 0.336
ID -0.002283 0.003865 -0.591 0.558
Residual standard error: 0.2716 on 37 degrees of freedom
Multiple R-squared: 0.009345, Adjusted R-squared: -0.01743
F-statistic: 0.349 on 1 and 37 DF, p-value: 0.5582
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12821, p-value = 0.9114
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.4059, p-value = 0.8686
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.22823, df = 1, p-value = 0.6328
Box-Ljung test
data: lm_residuals
X-squared = 1.8399, df = 1, p-value = 0.175
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.63330 -0.11817 0.00993 0.11824 0.58332
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0196636 0.0519916 -0.378 0.706
ID 0.0002116 0.0011016 0.192 0.848
Residual standard error: 0.2318 on 79 degrees of freedom
Multiple R-squared: 0.0004669, Adjusted R-squared: -0.01219
F-statistic: 0.0369 on 1 and 79 DF, p-value: 0.8482
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 = 2.4861, p-value = 0.9827
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.063588, df = 1, p-value = 0.8009
Box-Ljung test
data: lm_residuals
X-squared = 5.0241, df = 1, p-value = 0.025
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.70172 -0.15647 0.00833 0.16073 0.77393
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.083974 0.068121 -1.233 0.223
ID 0.002822 0.001975 1.429 0.158
Residual standard error: 0.2583 on 57 degrees of freedom
Multiple R-squared: 0.03458, Adjusted R-squared: 0.01765
F-statistic: 2.042 on 1 and 57 DF, p-value: 0.1585
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 = 2.2417, p-value = 0.7881
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.041041, df = 1, p-value = 0.8395
Box-Ljung test
data: lm_residuals
X-squared = 0.99368, df = 1, p-value = 0.3188
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.62033 -0.12630 0.00226 0.11764 0.59122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0354745 0.0537381 -0.660 0.511
ID 0.0005281 0.0011819 0.447 0.656
Residual standard error: 0.235 on 76 degrees of freedom
Multiple R-squared: 0.00262, Adjusted R-squared: -0.0105
F-statistic: 0.1996 on 1 and 76 DF, p-value: 0.6563
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11538, p-value = 0.6802
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.5064, p-value = 0.9848
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.28198, df = 1, p-value = 0.5954
Box-Ljung test
data: lm_residuals
X-squared = 5.2876, df = 1, p-value = 0.02148