Analysis
[1] "景気動向指数個別系列:遅行系列:寄与度:消費者物価指数(生鮮食品を除く総合)(前年同月比):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 0.09
2000 -0.17 0.18 -0.16 -0.09 0.18 -0.09 0.00 0.00 -0.17 -0.08 0.10 -0.08
2001 -0.17 0.01 -0.08 0.10 -0.16 0.10 0.02 0.01 0.10 0.10 -0.07 -0.07
2002 0.10 0.02 0.10 -0.15 0.11 0.02 0.02 -0.06 0.03 0.03 0.11 0.11
2003 -0.06 0.11 0.11 0.18 0.00 0.00 0.18 0.09 -0.01 0.17 -0.19 0.09
2004 -0.09 0.09 -0.09 -0.09 -0.09 0.19 -0.09 0.00 0.19 -0.10 -0.10 0.00
2005 -0.10 -0.09 0.10 0.10 0.19 -0.20 0.00 0.10 -0.01 0.09 0.09 -0.01
2006 -0.21 0.09 0.08 -0.21 0.08 0.19 -0.02 0.08 -0.12 -0.12 0.09 -0.12
2007 -0.12 -0.12 -0.22 0.20 -0.01 -0.01 -0.01 -0.01 -0.01 0.20 0.30 0.28
2008 -0.03 0.19 0.19 -0.34 0.36 0.20 0.31 -0.03 -0.13 -0.43 -0.72 -0.69
2009 -0.19 0.01 -0.08 0.00 -0.50 -0.42 -0.33 -0.14 0.11 0.11 0.33 0.33
2010 0.01 0.10 0.01 -0.24 0.27 0.18 -0.07 0.10 -0.07 0.38 0.10 0.10
2011 -0.23 0.01 0.09 0.44 0.09 -0.08 0.27 0.09 0.00 -0.27 -0.08 0.09
2012 0.00 0.18 0.08 0.00 -0.27 -0.09 -0.09 0.00 0.18 0.09 -0.08 -0.08
2013 0.01 -0.07 -0.16 0.11 0.38 0.34 0.29 0.11 -0.08 0.19 0.27 0.07
2014 -0.03 -0.03 -0.03 0.30 0.15 -0.15 -0.06 -0.26 -0.16 -0.15 -0.24 -0.23
2015 -0.33 -0.22 0.17 -0.37 -0.22 -0.02 -0.12 -0.11 -0.02 -0.01 0.18 -0.01
2016 -0.21 0.09 -0.30 -0.09 0.01 0.00 -0.09 0.01 0.01 0.10 0.00 0.20
2017 0.29 0.10 0.00 0.10 0.09 -0.01 0.09 0.18 -0.01 0.09 0.09 -0.02
2018 -0.02 0.08 -0.13 -0.22 -0.01 0.10 0.00 0.10 0.10 0.00 -0.10 -0.19
2019 0.11 -0.09 0.11 0.12 -0.08 -0.18 0.02 -0.10 -0.21
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35114 -0.09165 0.00031 0.07691 0.38459
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.143644 0.053651 2.677 0.0110 *
ID -0.004644 0.002338 -1.986 0.0544 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1643 on 37 degrees of freedom
Multiple R-squared: 0.09636, Adjusted R-squared: 0.07194
F-statistic: 3.946 on 1 and 37 DF, p-value: 0.05444
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 = 1.8873, p-value = 0.2988
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.87181, df = 1, p-value = 0.3505
Box-Ljung test
data: lm_residuals
X-squared = 0.12731, df = 1, p-value = 0.7212
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.37193 -0.09161 0.00284 0.10724 0.36635
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0161975 0.0354313 0.457 0.649
ID -0.0005095 0.0007507 -0.679 0.499
Residual standard error: 0.158 on 79 degrees of freedom
Multiple R-squared: 0.005797, Adjusted R-squared: -0.006788
F-statistic: 0.4606 on 1 and 79 DF, p-value: 0.4993
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14815, p-value = 0.338
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.159, p-value = 0.00002039
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 8.7705, df = 1, p-value = 0.003061
Box-Ljung test
data: lm_residuals
X-squared = 14.252, df = 1, p-value = 0.0001599
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.66118 -0.11203 0.01788 0.12486 0.44410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.072022 0.062819 -1.147 0.256
ID 0.001887 0.001821 1.036 0.305
Residual standard error: 0.2382 on 57 degrees of freedom
Multiple R-squared: 0.01848, Adjusted R-squared: 0.001263
F-statistic: 1.073 on 1 and 57 DF, p-value: 0.3046
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 = 0.90192, p-value = 0.0000009176
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.174, df = 1, p-value = 0.001424
Box-Ljung test
data: lm_residuals
X-squared = 16.413, df = 1, p-value = 0.00005094
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.37922 -0.10046 0.00443 0.10713 0.35291
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0286480 0.0364351 0.786 0.434
ID -0.0007772 0.0008014 -0.970 0.335
Residual standard error: 0.1593 on 76 degrees of freedom
Multiple R-squared: 0.01222, Adjusted R-squared: -0.0007722
F-statistic: 0.9406 on 1 and 76 DF, p-value: 0.3352
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10256, p-value = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1385, p-value = 0.00001756
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.586, df = 1, p-value = 0.001139
Box-Ljung test
data: lm_residuals
X-squared = 14.348, df = 1, p-value = 0.000152