Analysis
[1] "景気動向指数個別系列:先行系列:投資環境指数(製造業):内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 1.86
2000 1.92 1.92 2.11 2.21 2.40 2.39 2.57 2.44 2.59 2.67 2.94 3.00
2001 2.98 3.00 2.86 2.55 2.34 2.10 1.72 1.41 1.10 1.09 0.90 0.76
2002 0.76 0.83 1.08 1.31 1.49 1.75 1.87 2.14 2.27 2.61 2.75 2.99
2003 3.00 2.96 2.97 3.05 3.10 2.79 2.79 2.38 2.60 2.53 2.70 2.65
2004 2.88 3.15 3.11 3.10 3.21 3.07 3.00 3.30 3.39 3.34 3.38 3.39
2005 3.61 3.56 3.81 3.86 3.83 3.90 3.76 3.72 3.57 3.53 3.65 3.65
2006 3.57 3.56 3.40 3.27 3.39 3.33 3.42 3.80 3.83 3.82 3.95 3.97
2007 3.92 3.95 3.90 3.90 3.70 3.52 3.49 3.59 3.42 3.45 3.56 3.50
2008 3.51 3.55 3.59 3.09 2.73 2.66 2.61 2.61 2.41 1.19 0.04 -0.99
2009 -2.19 -3.32 -4.55 -3.77 -2.92 -1.89 -1.33 -0.58 0.08 0.46 1.08 1.53
2010 1.74 2.00 2.14 2.28 2.31 2.49 2.51 2.59 2.64 2.65 2.37 2.45
2011 2.17 1.97 1.82 1.66 1.50 1.30 1.52 1.73 1.90 1.73 1.57 1.53
2012 1.61 1.70 1.74 1.84 1.91 1.92 1.96 1.94 1.96 1.88 1.89 1.72
2013 2.14 2.55 2.99 3.01 2.81 2.87 2.98 3.13 3.23 3.46 3.59 3.58
2014 3.74 3.84 3.83 3.54 3.28 2.97 3.18 3.38 3.50 3.64 3.75 3.91
2015 3.89 3.77 3.62 3.72 3.71 3.69 3.78 3.85 3.91 3.79 3.60 3.45
2016 3.52 3.58 3.46 3.57 3.66 3.82 3.78 3.65 3.68 3.90 4.06 4.27
2017 4.35 4.53 4.65 4.68 4.64 4.58 4.64 4.75 4.74 4.71 4.74 4.73
2018 4.70 4.74 4.74 4.72 4.70 4.66 4.51 4.35 4.22 4.11 4.04 4.04
2019 3.92 3.82 3.77 3.68 3.71 3.76
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.48773 -0.21583 0.04165 0.23765 0.73873
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.950769 0.145768 13.383 0.000000000000000942 ***
ID -0.003038 0.006352 -0.478 0.635
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4464 on 37 degrees of freedom
Multiple R-squared: 0.006147, Adjusted R-squared: -0.02071
F-statistic: 0.2288 on 1 and 37 DF, p-value: 0.6352
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25641, p-value = 0.1547
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.18332, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 9.5451, df = 1, p-value = 0.002005
Box-Ljung test
data: lm_residuals
X-squared = 24.14, df = 1, p-value = 0.000000896
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.98332 -0.28288 0.02888 0.34206 0.58965
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.104362 0.091621 33.883 < 0.0000000000000002 ***
ID 0.018961 0.002015 9.409 0.000000000000022 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4007 on 76 degrees of freedom
Multiple R-squared: 0.5381, Adjusted R-squared: 0.532
F-statistic: 88.54 on 1 and 76 DF, p-value: 0.00000000000002202
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.089744, p-value = 0.9147
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.1206, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.082, df = 1, p-value = 0.001497
Box-Ljung test
data: lm_residuals
X-squared = 61.664, df = 1, p-value = 0.000000000000004108
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-4.9961 -0.2933 -0.0562 1.0771 2.7109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02359 0.41859 -0.056 0.95525
ID 0.04270 0.01213 3.519 0.00086 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.587 on 57 degrees of freedom
Multiple R-squared: 0.1784, Adjusted R-squared: 0.164
F-statistic: 12.38 on 1 and 57 DF, p-value: 0.0008598
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25424, p-value = 0.04374
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.097231, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 17.43, df = 1, p-value = 0.0000298
Box-Ljung test
data: lm_residuals
X-squared = 53.087, df = 1, p-value = 0.0000000000003192
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.82007 -0.29057 0.00893 0.32043 0.58993
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.25907 0.08922 36.527 < 0.0000000000000002 ***
ID 0.01700 0.00204 8.333 0.00000000000333 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3825 on 73 degrees of freedom
Multiple R-squared: 0.4875, Adjusted R-squared: 0.4805
F-statistic: 69.44 on 1 and 73 DF, p-value: 0.000000000003335
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14667, p-value = 0.3974
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.10667, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 22.822, df = 1, p-value = 0.000001777
Box-Ljung test
data: lm_residuals
X-squared = 65.38, df = 1, p-value = 0.0000000000000006661
