Analysis
[1] "景気動向指数個別系列:一致系列:有効求人倍率(除学卒)(倍):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 0.50
2000 0.51 0.52 0.54 0.56 0.56 0.58 0.60 0.61 0.62 0.64 0.65 0.65
2001 0.65 0.64 0.63 0.62 0.61 0.61 0.60 0.58 0.57 0.54 0.52 0.51
2002 0.50 0.51 0.52 0.52 0.53 0.53 0.54 0.55 0.55 0.56 0.56 0.57
2003 0.58 0.59 0.60 0.61 0.61 0.62 0.63 0.65 0.67 0.70 0.72 0.75
2004 0.76 0.76 0.77 0.78 0.80 0.82 0.83 0.84 0.86 0.88 0.91 0.92
2005 0.91 0.91 0.93 0.94 0.94 0.95 0.96 0.96 0.96 0.98 0.99 1.01
2006 1.03 1.04 1.05 1.05 1.07 1.07 1.08 1.07 1.07 1.06 1.06 1.06
2007 1.06 1.05 1.05 1.07 1.07 1.07 1.06 1.05 1.03 1.01 0.98 0.98
2008 0.97 0.96 0.96 0.96 0.95 0.92 0.89 0.86 0.83 0.79 0.75 0.71
2009 0.64 0.57 0.52 0.49 0.46 0.44 0.43 0.42 0.43 0.44 0.44 0.44
2010 0.45 0.46 0.48 0.49 0.50 0.51 0.53 0.54 0.55 0.56 0.58 0.59
2011 0.60 0.62 0.62 0.62 0.61 0.62 0.64 0.65 0.67 0.69 0.71 0.72
2012 0.74 0.75 0.77 0.78 0.79 0.80 0.81 0.82 0.81 0.82 0.82 0.83
2013 0.84 0.85 0.87 0.88 0.90 0.92 0.93 0.95 0.96 0.99 1.01 1.03
2014 1.04 1.06 1.07 1.08 1.09 1.09 1.10 1.10 1.10 1.11 1.12 1.14
2015 1.15 1.16 1.16 1.16 1.18 1.19 1.20 1.22 1.23 1.24 1.26 1.27
2016 1.29 1.30 1.31 1.33 1.35 1.36 1.36 1.37 1.38 1.40 1.41 1.42
2017 1.43 1.45 1.46 1.48 1.49 1.50 1.51 1.52 1.52 1.55 1.56 1.58
2018 1.59 1.59 1.59 1.60 1.61 1.61 1.62 1.63 1.63 1.62 1.63 1.63
2019 1.63 1.63 1.63 1.63 1.62 1.61 1.59 1.59 1.57
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.028945 -0.007692 0.002332 0.010428 0.019777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412632 0.004406 93.66 <0.0000000000000002 ***
ID 0.011253 0.000192 58.62 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01349 on 37 degrees of freedom
Multiple R-squared: 0.9893, Adjusted R-squared: 0.9891
F-statistic: 3436 on 1 and 37 DF, p-value: < 0.00000000000000022
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 = 0.34659, p-value = 0.00000000000133
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 5.6803, df = 1, p-value = 0.01716
Box-Ljung test
data: lm_residuals
X-squared = 25.151, df = 1, p-value = 0.0000005301
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.16634 -0.01409 0.01071 0.02933 0.06282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8910000 0.0098311 90.63 <0.0000000000000002 ***
ID 0.0104363 0.0002083 50.10 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04383 on 79 degrees of freedom
Multiple R-squared: 0.9695, Adjusted R-squared: 0.9691
F-statistic: 2510 on 1 and 79 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1358, p-value = 0.4462
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.048602, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 13.131, df = 1, p-value = 0.0002905
Box-Ljung test
data: lm_residuals
X-squared = 63.917, df = 1, p-value = 0.000000000000001332
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.18986 -0.11065 -0.03390 0.09099 0.38642
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.560491 0.038398 14.597 < 0.0000000000000002 ***
ID 0.003085 0.001113 2.772 0.00752 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1456 on 57 degrees of freedom
Multiple R-squared: 0.1188, Adjusted R-squared: 0.1033
F-statistic: 7.683 on 1 and 57 DF, p-value: 0.007517
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 = 0.025858, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 22.327, df = 1, p-value = 0.0000023
Box-Ljung test
data: lm_residuals
X-squared = 52.356, df = 1, p-value = 0.0000000000004629
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.161659 -0.017568 0.005123 0.028525 0.063722
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.9316750 0.0098050 95.02 <0.0000000000000002 ***
ID 0.0102562 0.0002157 47.56 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04288 on 76 degrees of freedom
Multiple R-squared: 0.9675, Adjusted R-squared: 0.9671
F-statistic: 2262 on 1 and 76 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.1624
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.051849, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 14.617, df = 1, p-value = 0.0001317
Box-Ljung test
data: lm_residuals
X-squared = 60.896, df = 1, p-value = 0.000000000000005995