library(WDI)
tahıl <- WDI(country=c("US", "TR","JP"), indicator=c("AG.YLD.CREL.KG"), start=1960, end=2020)
names(tahıl) <- c("iso2c", "Ülke", "tahıl", "Sene")
head(tahıl)
## iso2c Ülke tahıl Sene
## 1 JP Japan NA 2020
## 2 JP Japan NA 2019
## 3 JP Japan 5918.8 2018
## 4 JP Japan 6048.9 2017
## 5 JP Japan 6082.9 2016
## 6 JP Japan 6091.2 2015
library(ggplot2)
ggplot(tahıl, aes(Sene, tahıl, color=Ülke, linetype=Ülke)) + geom_line()
## Warning: Removed 9 row(s) containing missing values (geom_path).
TR <- cbind(tahıl$tahıl[tahıl$Ülke == "Turkey"], tahıl$Sene[tahıl$Ülke == "Turkey"])
TR <- TR[order(TR[,2]),]
TR
## [,1] [,2]
## [1,] NA 1960
## [2,] 989.4 1961
## [3,] 1136.0 1962
## [4,] 1343.4 1963
## [5,] 1117.9 1964
## [6,] 1138.6 1965
## [7,] 1272.7 1966
## [8,] 1303.4 1967
## [9,] 1215.7 1968
## [10,] 1291.7 1969
## [11,] 1215.0 1970
## [12,] 1573.5 1971
## [13,] 1435.1 1972
## [14,] 1238.1 1973
## [15,] 1294.2 1974
## [16,] 1632.4 1975
## [17,] 1805.5 1976
## [18,] 1791.7 1977
## [19,] 1824.6 1978
## [20,] 1880.1 1979
## [21,] 1855.1 1980
## [22,] 1871.7 1981
## [23,] 1978.7 1982
## [24,] 1838.4 1983
## [25,] 1971.1 1984
## [26,] 1931.0 1985
## [27,] 2135.5 1986
## [28,] 2137.4 1987
## [29,] 2250.2 1988
## [30,] 1742.1 1989
## [31,] 2214.2 1990
## [32,] 2240.5 1991
## [33,] 2124.4 1992
## [34,] 2255.9 1993
## [35,] 1922.3 1994
## [36,] 2057.3 1995
## [37,] 2112.3 1996
## [38,] 2151.3 1997
## [39,] 2387.5 1998
## [40,] 2099.8 1999
## [41,] 2370.6 2000
## [42,] 2178.3 2001
## [43,] 2242.0 2002
## [44,] 2308.6 2003
## [45,] 2477.9 2004
## [46,] 2634.4 2005
## [47,] 2661.9 2006
## [48,] 2410.2 2007
## [49,] 2601.3 2008
## [50,] 2808.0 2009
## [51,] 2727.1 2010
## [52,] 2970.0 2011
## [53,] 2958.3 2012
## [54,] 3256.9 2013
## [55,] 2831.5 2014
## [56,] 3307.8 2015
## [57,] 3105.4 2016
## [58,] 3257.4 2017
## [59,] 3163.9 2018
## [60,] NA 2019
## [61,] NA 2020
TR <- ts(TR[,1], start=min(tahıl$Sene), end=max(tahıl$Sene))
plot(TR, ylab="tahıl", 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 "ts" data:
## Start = 1962, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -535.45 -82.55 4.19 119.80 480.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 149.03349 94.11085 1.584 0.119
## L(TR, 1) 0.94593 0.04405 21.476 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 199.6 on 55 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.8935, Adjusted R-squared: 0.8915
## F-statistic: 461.2 on 1 and 55 DF, p-value: < 2.2e-16
Ikincigecikme <- dynlm(TR ~ L(TR, 2))
summary(Ikincigecikme)
##
## Time series regression with "ts" data:
## Start = 1963, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -467.84 -116.13 13.46 117.59 410.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 154.79140 93.71035 1.652 0.104
## L(TR, 2) 0.96152 0.04439 21.662 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 193.7 on 54 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.8968, Adjusted R-squared: 0.8949
## F-statistic: 469.2 on 1 and 54 DF, p-value: < 2.2e-16
Ucuncugecikme <- dynlm(TR ~ L(TR, 3))
summary(Ucuncugecikme)
##
## Time series regression with "ts" data:
## Start = 1964, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -498.73 -149.62 -20.58 188.16 447.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 186.84775 107.63272 1.736 0.0884 .
## L(TR, 3) 0.96183 0.05153 18.666 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 217.8 on 53 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.868, Adjusted R-squared: 0.8655
## F-statistic: 348.4 on 1 and 53 DF, p-value: < 2.2e-16
AR10 <- dynlm(TR ~ L(TR, c(1:10)))
summary(AR10)
##
## Time series regression with "ts" data:
## Start = 1971, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, c(1:10)))
##
## Residuals:
## Min 1Q Median 3Q Max
## -444.67 -88.57 3.98 131.24 277.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 116.96525 117.22474 0.998 0.3249
## L(TR, c(1:10))zoo(coredata(x), tt).1 0.22805 0.16303 1.399 0.1702
## L(TR, c(1:10))zoo(coredata(x), tt).2 0.33265 0.16689 1.993 0.0536 .
## L(TR, c(1:10))zoo(coredata(x), tt).3 -0.01693 0.17483 -0.097 0.9234
## L(TR, c(1:10))zoo(coredata(x), tt).4 0.22356 0.17817 1.255 0.2174
## L(TR, c(1:10))zoo(coredata(x), tt).5 0.29460 0.18624 1.582 0.1222
## L(TR, c(1:10))zoo(coredata(x), tt).6 0.01645 0.19139 0.086 0.9320
## L(TR, c(1:10))zoo(coredata(x), tt).7 0.04692 0.18881 0.249 0.8051
## L(TR, c(1:10))zoo(coredata(x), tt).8 0.05165 0.18324 0.282 0.7796
## L(TR, c(1:10))zoo(coredata(x), tt).9 -0.09344 0.18300 -0.511 0.6127
## L(TR, c(1:10))zoo(coredata(x), tt).10 -0.09415 0.17313 -0.544 0.5898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 179 on 37 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.9056, Adjusted R-squared: 0.8801
## F-statistic: 35.49 on 10 and 37 DF, p-value: 6.042e-16
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
## -12.3181 -2.5556 -0.0165 4.3529 9.3345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.16493 0.55882 18.19 <2e-16 ***
## x 0.71204 0.06207 11.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.065 on 198 degrees of freedom
## Multiple R-squared: 0.3992, Adjusted R-squared: 0.3962
## F-statistic: 131.6 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
## -2.2885 -0.7890 0.0608 0.6951 3.1722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08391 0.06961 1.206 0.229
## d(x) -0.08206 0.06396 -1.283 0.201
##
## Residual standard error: 0.9815 on 197 degrees of freedom
## Multiple R-squared: 0.008287, Adjusted R-squared: 0.003253
## F-statistic: 1.646 on 1 and 197 DF, p-value: 0.201