Analysis
[1] "景気動向指数個別系列:先行系列:総資本営業利益率(製造業)(%):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 3.51
2000 3.63 3.76 3.88 3.97 4.06 4.15 4.25 4.34 4.43 4.49 4.56 4.64
2001 4.48 4.30 4.13 3.84 3.58 3.31 3.05 2.79 2.52 2.39 2.26 2.13
2002 2.24 2.36 2.48 2.68 2.88 3.07 3.19 3.32 3.45 3.60 3.75 3.89
2003 3.81 3.74 3.67 3.66 3.63 3.61 3.72 3.85 3.98 4.00 4.01 4.01
2004 4.20 4.37 4.55 4.64 4.74 4.85 4.85 4.84 4.83 4.83 4.83 4.83
2005 4.93 5.03 5.13 5.10 5.08 5.07 5.07 5.06 5.05 5.08 5.10 5.12
2006 5.13 5.15 5.17 5.19 5.22 5.25 5.34 5.42 5.50 5.54 5.60 5.65
2007 5.62 5.58 5.55 5.52 5.45 5.39 5.28 5.19 5.10 5.05 5.02 5.00
2008 4.95 4.91 4.87 4.67 4.47 4.27 4.14 4.02 3.89 2.67 1.44 0.18
2009 -0.92 -2.05 -3.21 -2.34 -1.44 -0.54 0.09 0.73 1.38 1.87 2.34 2.82
2010 3.06 3.30 3.54 3.56 3.57 3.58 3.57 3.57 3.57 3.57 3.56 3.56
2011 3.39 3.23 3.08 2.86 2.65 2.43 2.60 2.76 2.92 2.78 2.64 2.51
2012 2.58 2.66 2.73 2.73 2.74 2.75 2.74 2.74 2.73 2.66 2.59 2.52
2013 2.88 3.22 3.55 3.61 3.67 3.73 3.78 3.85 3.91 4.05 4.19 4.32
2014 4.36 4.42 4.47 4.16 3.85 3.54 3.71 3.87 4.03 4.09 4.17 4.24
2015 4.17 4.10 4.02 4.06 4.10 4.15 4.19 4.23 4.26 4.09 3.90 3.72
2016 3.62 3.51 3.41 3.48 3.54 3.59 3.58 3.58 3.59 3.85 4.08 4.31
2017 4.44 4.58 4.72 4.70 4.68 4.66 4.72 4.76 4.80 4.78 4.78 4.78
2018 4.78 4.79 4.79 4.77 4.73 4.69 4.57 4.46 4.35 4.24 4.13 4.03
2019 3.92 3.80 3.67 3.63 3.61 3.59
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.41341 -0.15423 0.00783 0.35693 0.52207
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.300945 0.131782 25.048 < 0.0000000000000002 ***
ID -0.017534 0.005742 -3.054 0.00418 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4036 on 37 degrees of freedom
Multiple R-squared: 0.2013, Adjusted R-squared: 0.1797
F-statistic: 9.324 on 1 and 37 DF, p-value: 0.004175
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 = 0.17335, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 9.6928, df = 1, p-value = 0.00185
Box-Ljung test
data: lm_residuals
X-squared = 23.469, df = 1, p-value = 0.00000127
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.91806 -0.31783 0.06303 0.39466 0.56369
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.790233 0.096620 39.228 < 0.0000000000000002 ***
ID 0.007826 0.002125 3.683 0.00043 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4226 on 76 degrees of freedom
Multiple R-squared: 0.1514, Adjusted R-squared: 0.1403
F-statistic: 13.56 on 1 and 76 DF, p-value: 0.0004297
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 = 0.546
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.089451, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 8.7901, df = 1, p-value = 0.003029
Box-Ljung test
data: lm_residuals
X-squared = 65.643, df = 1, p-value = 0.0000000000000005551
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-5.0609 -0.2957 0.0154 1.1183 2.9071
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.53406 0.42248 3.631 0.000606 ***
ID 0.02880 0.01225 2.352 0.022162 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.602 on 57 degrees of freedom
Multiple R-squared: 0.08845, Adjusted R-squared: 0.07246
F-statistic: 5.531 on 1 and 57 DF, p-value: 0.02216
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 = 0.094483, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 17.888, df = 1, p-value = 0.00002343
Box-Ljung test
data: lm_residuals
X-squared = 52.924, df = 1, p-value = 0.0000000000003467
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.76097 -0.30221 0.05164 0.39087 0.57180
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.912515 0.095110 41.137 < 0.0000000000000002 ***
ID 0.005846 0.002175 2.688 0.00889 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4077 on 73 degrees of freedom
Multiple R-squared: 0.09007, Adjusted R-squared: 0.07761
F-statistic: 7.226 on 1 and 73 DF, p-value: 0.008894
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13333, p-value = 0.5204
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.081987, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 19.629, df = 1, p-value = 0.000009403
Box-Ljung test
data: lm_residuals
X-squared = 67.676, df = 1, p-value = 0.000000000000000222
