library(WDI)
tahıl <- WDI(country=c("US", "TR","JP"), indicator=c("AG.YLD.CREL.KG"), start=2000, end=2021)
names(tahil) <- c("iso2c", "Ülke", "tahıl", "Sene")
head(tahil)
## iso2c Ülke tahil 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 6 row(s) containing missing values (geom_path).
## 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,] 2370.6 2000
## [2,] 2178.3 2001
## [3,] 2242.0 2002
## [4,] 2308.6 2003
## [5,] 2477.9 2004
## [6,] 2634.4 2005
## [7,] 2661.9 2006
## [8,] 2410.2 2007
## [9,] 2601.3 2008
## [10,] 2808.0 2009
## [11,] 2727.1 2010
## [12,] 2970.0 2011
## [13,] 2958.3 2012
## [14,] 3256.9 2013
## [15,] 2831.5 2014
## [16,] 3307.8 2015
## [17,] 3105.4 2016
## [18,] 3257.4 2017
## [19,] 3163.9 2018
## [20,] NA 2019
## [21,] NA 2020
TR <- ts(TR[,1], start=min(tahıl$Sene), end=max(tahıl$Sene))
plot(TR, ylab="tahıl", xlab="Sene")
library(dynlm)
## Loading required package: 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 = 2001, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -371.57 -103.20 -22.47 126.38 451.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 549.2550 405.8266 1.353 0.195
## L(TR, 1) 0.8148 0.1475 5.523 4.64e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 218.9 on 16 degrees of freedom
## (0 observations deleted due to missingness)
## Multiple R-squared: 0.6559, Adjusted R-squared: 0.6344
## F-statistic: 30.5 on 1 and 16 DF, p-value: 4.636e-05
Ikincigecikme <- dynlm(TR ~ L(TR, 2))
summary(Ikincigecikme)
##
## Time series regression with "ts" data:
## Start = 2002, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -343.59 -70.38 23.41 125.49 245.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 508.3253 348.9043 1.457 0.166
## L(TR, 2) 0.8524 0.1284 6.639 7.88e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 177.2 on 15 degrees of freedom
## (0 observations deleted due to missingness)
## Multiple R-squared: 0.7461, Adjusted R-squared: 0.7292
## F-statistic: 44.08 on 1 and 15 DF, p-value: 7.883e-06
Ucuncugecikme <- dynlm(TR ~ L(TR, 3))
summary(Ucuncugecikme)
##
## Time series regression with "ts" data:
## Start = 2003, End = 2018
##
## Call:
## dynlm(formula = TR ~ L(TR, 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -324.40 -162.36 -0.23 112.30 375.69
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 982.4705 450.6331 2.180 0.046803 *
## L(TR, 3) 0.6963 0.1674 4.159 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 220.1 on 14 degrees of freedom
## (0 observations deleted due to missingness)
## Multiple R-squared: 0.5527, Adjusted R-squared: 0.5207
## F-statistic: 17.3 on 1 and 14 DF, p-value: 0.0009649
AR10 <- dynlm(TR ~ L(TR, c(1:10)))
summary(AR10)
##
## Time series regression with "ts" data:
## Start = 2010, End = 2018
##
## 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) 7287.8778 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).1 -5.6893 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).2 -2.3362 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).3 2.9441 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).4 1.2039 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).5 0.5612 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).6 -0.1430 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).7 -1.6616 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).8 4.4173 NaN NaN NaN
## L(TR, c(1:10))zoo(coredata(x), tt).9 NA NA NA NA
## L(TR, c(1:10))zoo(coredata(x), tt).10 NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (0 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
## -15.8028 -3.6840 0.3679 4.5184 14.2050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.7528 0.4476 30.72 <2e-16 ***
## x 1.5226 0.1085 14.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.257 on 198 degrees of freedom
## Multiple R-squared: 0.4987, Adjusted R-squared: 0.4962
## F-statistic: 197 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
## -2.36716 -0.72029 0.06349 0.67425 2.41554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12359 0.06808 1.815 0.071 .
## d(x) -0.01592 0.06821 -0.233 0.816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9604 on 197 degrees of freedom
## Multiple R-squared: 0.0002764, Adjusted R-squared: -0.004798
## F-statistic: 0.05446 on 1 and 197 DF, p-value: 0.8157