Analysis
[1] "景気ウォッチャー調査:関東:東京都:季節調整値:景気の先行き判断(方向性)DI:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 36.9 40.1 46.7 50.7 51.7 45.1 44.7 44.3 43.1 37.9 42.9 40.0
2003 41.5 44.4 35.9 39.8 39.2 47.9 49.9 53.3 53.8 54.9 54.0 50.9
2004 56.7 57.2 57.2 58.2 58.2 57.7 56.5 53.6 51.6 52.9 53.7 53.7
2005 52.7 51.8 53.0 51.6 52.7 52.0 52.7 54.1 56.3 58.3 58.1 60.8
2006 61.9 61.0 58.4 57.8 58.0 53.0 54.0 54.3 53.9 55.5 54.1 54.9
2007 53.4 53.1 51.4 53.6 50.5 50.3 48.3 49.0 48.9 46.7 45.8 45.8
2008 37.8 41.8 40.9 36.0 31.5 31.2 32.6 34.1 33.0 27.4 30.6 25.8
2009 26.0 27.9 35.5 38.9 42.0 45.2 46.0 45.7 46.9 48.9 42.8 45.1
2010 46.8 47.8 47.5 46.9 48.0 46.9 47.3 43.5 45.8 48.0 49.4 50.8
2011 52.9 47.1 26.0 36.6 44.1 46.7 46.8 48.5 45.1 46.7 47.3 49.1
2012 49.8 50.7 49.9 50.6 44.9 43.6 47.5 48.6 47.1 45.5 45.2 57.1
2013 58.5 58.9 58.6 56.8 54.6 53.4 54.1 53.5 57.1 56.9 56.7 58.2
2014 53.1 41.7 36.3 51.5 53.6 53.6 54.6 55.1 52.3 50.6 47.9 51.1
2015 51.7 52.6 54.5 55.3 55.5 55.7 53.0 51.0 51.1 50.9 49.2 50.1
2016 49.4 46.1 46.2 45.9 47.1 37.1 47.9 49.8 51.0 51.1 52.1 50.9
2017 51.0 53.4 49.0 52.9 52.9 53.1 53.4 53.0 52.6 56.5 55.2 54.3
2018 55.4 53.9 49.7 50.8 52.3 51.2 51.5 54.0 52.2 52.7 53.3 49.6
2019 50.1 51.1 47.3 49.0 46.3 49.4 46.7 43.5 38.5 47.3
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-20.6174 -1.2590 0.8388 1.9933 9.1847
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.50499 1.53005 29.741 <0.0000000000000002 ***
ID 0.06180 0.06667 0.927 0.36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.686 on 37 degrees of freedom
Multiple R-squared: 0.0227, Adjusted R-squared: -0.003718
F-statistic: 0.8593 on 1 and 37 DF, p-value: 0.36
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 = 1.1765, p-value = 0.001895
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.014986, df = 1, p-value = 0.9026
Box-Ljung test
data: lm_residuals
X-squared = 5.244, df = 1, p-value = 0.02202
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.8686 -1.4740 0.4842 2.5685 6.1577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.15456 0.91987 58.872 < 0.0000000000000002 ***
ID -0.06573 0.01925 -3.414 0.00101 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.127 on 80 degrees of freedom
Multiple R-squared: 0.1271, Adjusted R-squared: 0.1162
F-statistic: 11.65 on 1 and 80 DF, p-value: 0.001008
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.2561, p-value = 0.008991
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.73107, p-value = 0.000000000015
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.59045, df = 1, p-value = 0.4422
Box-Ljung test
data: lm_residuals
X-squared = 33.381, df = 1, p-value = 0.000000007576
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.7580 -2.8268 0.4121 4.7770 8.9349
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.83144 1.53873 21.987 < 0.0000000000000002 ***
ID 0.34076 0.04461 7.639 0.000000000274 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.835 on 57 degrees of freedom
Multiple R-squared: 0.5059, Adjusted R-squared: 0.4972
F-statistic: 58.36 on 1 and 57 DF, p-value: 0.0000000002735
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 = 0.57528, p-value = 0.00000000001417
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.47026, df = 1, p-value = 0.4929
Box-Ljung test
data: lm_residuals
X-squared = 30.834, df = 1, p-value = 0.00000002811
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.303 -1.357 0.534 2.520 6.126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.22512 0.92885 57.302 <0.0000000000000002 ***
ID -0.05183 0.02017 -2.569 0.0121 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.089 on 77 degrees of freedom
Multiple R-squared: 0.07896, Adjusted R-squared: 0.067
F-statistic: 6.601 on 1 and 77 DF, p-value: 0.01212
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12658, p-value = 0.5543
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.77133, p-value = 0.0000000001836
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.53084, df = 1, p-value = 0.4663
Box-Ljung test
data: lm_residuals
X-squared = 30.319, df = 1, p-value = 0.00000003665