library(WDI)
hasılat <- WDI(country=c("US", "TR","JP"), indicator=c("GC.REV.XGRT.GD.ZS"), start=1960, end=2020)
names(hasılat) <- c("iso2c", "Ülke", "hasılat", "Sene")
head(hasılat)
## iso2c Ülke hasılat Sene
## 1 JP Japan NA 2020
## 2 JP Japan NA 2019
## 3 JP Japan NA 2018
## 4 JP Japan NA 2017
## 5 JP Japan NA 2016
## 6 JP Japan NA 2015
library(ggplot2)
ggplot(hasılat, aes(Sene, hasılat, color=Ülke, linetype=Ülke)) + geom_line()
## Warning: Removed 82 row(s) containing missing values (geom_path).
TR <- cbind(hasılat$hasılat[hasılat$Ülke == "Turkey"], hasılat$Sene[hasılat$Ülke == "Turkey"])
TR <- TR[order(TR[,2]),]
TR
## [,1] [,2]
## [1,] NA 1960
## [2,] NA 1961
## [3,] NA 1962
## [4,] NA 1963
## [5,] NA 1964
## [6,] NA 1965
## [7,] NA 1966
## [8,] NA 1967
## [9,] NA 1968
## [10,] NA 1969
## [11,] NA 1970
## [12,] NA 1971
## [13,] 16.39571 1972
## [14,] 16.29121 1973
## [15,] 14.44131 1974
## [16,] 16.87868 1975
## [17,] 17.45627 1976
## [18,] 17.69384 1977
## [19,] 18.42089 1978
## [20,] 17.87913 1979
## [21,] 18.07250 1980
## [22,] 18.26984 1981
## [23,] NA 1982
## [24,] 16.56719 1983
## [25,] 12.32844 1984
## [26,] 13.73538 1985
## [27,] 13.73590 1986
## [28,] 13.88067 1987
## [29,] 13.50711 1988
## [30,] 13.67472 1989
## [31,] 13.65559 1990
## [32,] 14.29936 1991
## [33,] 16.05745 1992
## [34,] 17.85170 1993
## [35,] 19.25937 1994
## [36,] 17.93984 1995
## [37,] 18.33879 1996
## [38,] 21.87729 1997
## [39,] 17.21930 1998
## [40,] NA 1999
## [41,] NA 2000
## [42,] NA 2001
## [43,] NA 2002
## [44,] NA 2003
## [45,] NA 2004
## [46,] NA 2005
## [47,] NA 2006
## [48,] NA 2007
## [49,] 30.83969 2008
## [50,] 31.63349 2009
## [51,] 31.53884 2010
## [52,] 30.66820 2011
## [53,] 30.78838 2012
## [54,] 30.83433 2013
## [55,] 30.33342 2014
## [56,] 30.53091 2015
## [57,] 31.00342 2016
## [58,] 29.48089 2017
## [59,] 30.51964 2018
## [60,] 30.63394 2019
## [61,] 30.38186 2020
TR <- ts(TR[,1], start=min(hasılat$Sene), end=max(hasılat$Sene))
plot(TR, ylab="hasılat", xlab="Sene")
library(dynlm)
## Zorunlu paket yükleniyor: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
Ilkgecikme <- dynlm(TR ~ L(TR, 1))
summary(Ilkgecikme)
##
## Time series regression with "zooreg" data:
## Start = 1973, End = 2020
##
## Call:
## dynlm(formula = TR ~ L(TR, 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6915 -0.3015 0.1187 0.6005 3.3980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.69526 0.79903 0.87 0.39
## L(TR, 1) 0.96975 0.03592 27.00 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.527 on 34 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.9554, Adjusted R-squared: 0.9541
## F-statistic: 728.7 on 1 and 34 DF, p-value: < 2.2e-16
Ikincigecikme <- dynlm(TR ~ L(TR, 2))
summary(Ikincigecikme)
##
## Time series regression with "zooreg" data:
## Start = 1974, End = 2020
##
## Call:
## dynlm(formula = TR ~ L(TR, 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3927 -0.7584 -0.1454 0.5632 3.4567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.52819 0.79156 1.931 0.0624 .
## L(TR, 2) 0.94162 0.03604 26.126 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.49 on 32 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.9552, Adjusted R-squared: 0.9538
## F-statistic: 682.6 on 1 and 32 DF, p-value: < 2.2e-16
Ucuncugecikme <- dynlm(TR ~ L(TR, 3))
summary(Ucuncugecikme)
##
## Time series regression with "zooreg" data:
## Start = 1975, End = 2020
##
## Call:
## dynlm(formula = TR ~ L(TR, 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5099 -0.4763 0.0117 0.7124 4.0553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.11560 1.03799 2.038 0.0504 .
## L(TR, 3) 0.91532 0.04772 19.181 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.913 on 30 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.9246, Adjusted R-squared: 0.9221
## F-statistic: 367.9 on 1 and 30 DF, p-value: < 2.2e-16
AR10 <- dynlm(TR ~ L(TR, c(1:10)))
summary(AR10)
##
## Time series regression with "zooreg" data:
## Start = 1993, End = 2020
##
## Call:
## dynlm(formula = TR ~ L(TR, c(1:10)))
##
## Residuals:
## ALL 9 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.2859 NaN NaN NaN
## L(TR, c(1:10))1 0.2481 NaN NaN NaN
## L(TR, c(1:10))2 -1.1813 NaN NaN NaN
## L(TR, c(1:10))3 1.1446 NaN NaN NaN
## L(TR, c(1:10))4 -0.5135 NaN NaN NaN
## L(TR, c(1:10))5 2.1935 NaN NaN NaN
## L(TR, c(1:10))6 -4.4734 NaN NaN NaN
## L(TR, c(1:10))7 1.7489 NaN NaN NaN
## L(TR, c(1:10))8 1.4787 NaN NaN NaN
## L(TR, c(1:10))9 NA NA NA NA
## L(TR, c(1:10))10 NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 8 and 0 DF, p-value: NA
n<-200
u <- ts(rnorm(n))
v <- ts(rnorm(n))
y <- ts(rep(0,n))
for (t in 2:n){
y[t]<- y[t-1]+u[t]
}
x <- ts(rep(0,n))
for (t in 2:n){
x[t]<- x[t-1]+v[t]
}
plot(y,type='l', ylab="y[t-1]+u[t]")
plot(x,type='l', ylab="x[t-1]+v[t]")
Spurious <- lm(y~x)
summary(Spurious)
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4082 -3.3099 -0.9423 3.3274 11.0175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.4053 0.4211 -17.59 <2e-16 ***
## x -0.7765 0.0673 -11.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.484 on 198 degrees of freedom
## Multiple R-squared: 0.402, Adjusted R-squared: 0.399
## F-statistic: 133.1 on 1 and 198 DF, p-value: < 2.2e-16
duragan <- dynlm(d(y) ~ d(x))
summary(duragan)
##
## Time series regression with "ts" data:
## Start = 2, End = 200
##
## Call:
## dynlm(formula = d(y) ~ d(x))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.41421 -0.67855 -0.07528 0.64812 2.80565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.099472 0.068904 -1.444 0.150
## d(x) 0.003962 0.069178 0.057 0.954
##
## Residual standard error: 0.9711 on 197 degrees of freedom
## Multiple R-squared: 1.665e-05, Adjusted R-squared: -0.005059
## F-statistic: 0.00328 on 1 and 197 DF, p-value: 0.9544