Analysis
[1] "景気動向指数個別系列:遅行系列: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 -0.1
2000 -0.3 -0.1 -0.3 -0.4 -0.2 -0.3 -0.3 -0.3 -0.5 -0.6 -0.5 -0.6
2001 -0.8 -0.8 -0.9 -0.8 -1.0 -0.9 -0.9 -0.9 -0.8 -0.7 -0.8 -0.9
2002 -0.8 -0.8 -0.7 -0.9 -0.8 -0.8 -0.8 -0.9 -0.9 -0.9 -0.8 -0.7
2003 -0.8 -0.7 -0.6 -0.4 -0.4 -0.4 -0.2 -0.1 -0.1 0.1 -0.1 0.0
2004 -0.1 0.0 -0.1 -0.2 -0.3 -0.1 -0.2 -0.2 0.0 -0.1 -0.2 -0.2
2005 -0.3 -0.4 -0.3 -0.2 0.0 -0.2 -0.2 -0.1 -0.1 0.0 0.1 0.1
2006 -0.1 0.0 0.1 -0.1 0.0 0.2 0.2 0.3 0.2 0.1 0.2 0.1
2007 0.0 -0.1 -0.3 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 0.1 0.4 0.8
2008 0.8 1.0 1.2 0.9 1.5 1.9 2.4 2.4 2.3 1.9 1.0 0.2
2009 0.0 0.0 -0.1 -0.1 -1.1 -1.7 -2.2 -2.4 -2.3 -2.2 -1.7 -1.3
2010 -1.3 -1.2 -1.2 -1.5 -1.2 -1.0 -1.1 -1.0 -1.1 -0.6 -0.5 -0.4
2011 -0.8 -0.8 -0.7 -0.2 -0.1 -0.2 0.1 0.2 0.2 -0.1 -0.2 -0.1
2012 -0.1 0.1 0.2 0.2 -0.1 -0.2 -0.3 -0.3 -0.1 0.0 -0.1 -0.2
2013 -0.2 -0.3 -0.5 -0.4 0.0 0.4 0.7 0.8 0.7 0.9 1.2 1.3
2014 1.3 1.3 1.3 3.2 3.4 3.3 3.3 3.1 3.0 2.9 2.7 2.5
2015 2.2 2.0 2.2 0.3 0.1 0.1 0.0 -0.1 -0.1 -0.1 0.1 0.1
2016 -0.1 0.0 -0.3 -0.4 -0.4 -0.4 -0.5 -0.5 -0.5 -0.4 -0.4 -0.2
2017 0.1 0.2 0.2 0.3 0.4 0.4 0.5 0.7 0.7 0.8 0.9 0.9
2018 0.9 1.0 0.9 0.7 0.7 0.8 0.8 0.9 1.0 1.0 0.9 0.7
2019 0.8 0.7 0.8 0.9 0.8 0.6 0.6 0.5 0.3
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.80333 -0.21797 -0.02985 0.24529 0.59999
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.441970 0.106425 -13.549 0.000000000000000643 ***
ID 0.045304 0.004637 9.769 0.000000000008637938 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3259 on 37 degrees of freedom
Multiple R-squared: 0.7206, Adjusted R-squared: 0.7131
F-statistic: 95.44 on 1 and 37 DF, p-value: 0.000000000008638
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 = 0.43675, p-value = 0.0000000001134
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.082674, df = 1, p-value = 0.7737
Box-Ljung test
data: lm_residuals
X-squared = 18.58, df = 1, p-value = 0.00001629
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.56452 -0.77767 0.06985 0.32722 2.45022
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.089105 0.224789 4.845 0.0000062 ***
ID -0.008196 0.004763 -1.721 0.0892 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.002 on 79 degrees of freedom
Multiple R-squared: 0.03613, Adjusted R-squared: 0.02393
F-statistic: 2.961 on 1 and 79 DF, p-value: 0.0892
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 = 0.11347, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.231, df = 1, p-value = 0.00001957
Box-Ljung test
data: lm_residuals
X-squared = 73.127, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-2.2217 -0.7648 0.1829 0.4316 2.4667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.029515 0.283536 -0.104 0.917
ID -0.009299 0.008219 -1.131 0.263
Residual standard error: 1.075 on 57 degrees of freedom
Multiple R-squared: 0.02196, Adjusted R-squared: 0.004802
F-statistic: 1.28 on 1 and 57 DF, p-value: 0.2627
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.08481, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 23.199, df = 1, p-value = 0.000001461
Box-Ljung test
data: lm_residuals
X-squared = 54.78, df = 1, p-value = 0.0000000000001348
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.6765 -0.8512 0.1051 0.4054 2.2862
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.288978 0.223186 5.775 0.000000159 ***
ID -0.012509 0.004909 -2.548 0.0128 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9761 on 76 degrees of freedom
Multiple R-squared: 0.07872, Adjusted R-squared: 0.06659
F-statistic: 6.494 on 1 and 76 DF, p-value: 0.01284
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.21795, p-value = 0.04892
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.12373, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.53, df = 1, p-value = 0.00001673
Box-Ljung test
data: lm_residuals
X-squared = 68.402, df = 1, p-value < 0.00000000000000022