R Programming/Time Series
Introduction
editIn the following examples we will use the data set Mpyr which is included in the R-package Ecdat, which can be loaded into R and viewed in R by the following code.
#Installs the package Ecdat.
install.packages("Ecdat")
#Loads the packages Ecdat.
library(Ecdat)
#Attached the dataset Mpyr.
data(Mpyr)
#Shows the dataset Mpyr.
Mpyr
Time Series:
Start = 1900
End = 1989
Frequency = 1
m p y r
1900 1.718774 2.092641 0.9030195 4.380000
1901 1.856318 2.086574 1.0131038 4.280000
1902 1.936512 2.120476 1.0114817 4.920000
Creating time-series objects
edit- The function ts() is used to create time-series objects.
- The function as.ts() coerces an object to a time-series.
- The function is.ts() tests whether an object is a time-series.
Example:
> data.a<-seq(1,24,by=1)
> is.ts(data.a)
[1] FALSE
> ts(data.a, start=c(2005,1), frequency=12)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 1 2 3 4 5 6 7 8 9 10 11 12
2006 13 14 15 16 17 18 19 20 21 22 23 24
> data.b<-seq(1,24,by=1)
> is.ts(data.b)
[1] FALSE
> is.ts(as.ts(data.b))
[1] TRUE
Creating lagged and differenced variables
edit- The function lag() creates a lagged variable.
- The function diff() creates a differenced variable.
Example:
> data.a<-seq(1,12,by=1)
> ts.a<-ts(data.a, start=c(2005,1), frequency=4)
> lag.a<-lag(ts.a,k=1)
> diff.a<-diff(ts.a,lag=1,difference=1)
> ts.a
Qtr1 Qtr2 Qtr3 Qtr4
2005 1 2 3 4
2006 5 6 7 8
2007 9 10 11 12
> lag.a
Qtr1 Qtr2 Qtr3 Qtr4
2004 1
2005 2 3 4 5
2006 6 7 8 9
2007 10 11 12
> diff.a
Qtr1 Qtr2 Qtr3 Qtr4
2005 1 1 1
2006 1 1 1 1
2007 1 1 1 1
Plotting time-series objects
edit- The function plot.ts() is used for plotting time-series objects.
Fit Autoregressive Models to Time-series by OLS
editIn order to fit an autoregressive time series model to the data by ordinary least squares it is possible to use the function ar.ols() which is part of the "stats" package.
Autocorrelation function
editThe function acf() computes (and by default plots) estimates of the autocovariance or autocorrelation function. Function pacf() is the function used for the partial autocorrelations. Function ccf() computes the cross-correlation or cross-covariance of two univariate series.[1]
Useful R-packages
edit- fBasics, tis, zoo, tseries, xts, urca, forecast