Download Computer exercises # 3 in R 1. Firstly, you should install following

Computer exercises # 3 in R
1. Firstly, you should install following packages that are not included in R by default:
Package
lmtest
car
sandwich
moments
forecast
tseries
Description
Testing Linear Regression Models
Companion to Applied Regression
Robust Covariance Matrix Estimator
Moments, Cumulants, Skewness, Kurtosis and Related Tests
Forecasting Function for Time Series and Linear Models
Time Series Analysis and Computational Finance
These packages provide some functions required for diagnostic checking and significance testing after
estimation of a certain econometric model using LS method (post-estimation procedure).
You can install one or more packages by commands:
install.packages("lmtest")
install.packages("car")
install.packages("sandwich")
install.packages("moments")
install.packages("forecast")
install.packages("tseries")
Installed packages are stored in a library and they should be loaded using commands:
library(lmtest)
library(car)
library(sandwich)
library(moments)
library(forecast)
library(tseries)
Data files you want to use should be stored in the current working directory. Current working directory
can be located using command:
getwd()
The working directory can be changed:
setwd("C:/...")
R is able to read data from many formats. The most common format is a text file with data separated
into columns and with a header above each column describing the data. When importing dataframe
from the text file make sure that decimal places of the numbers are separated by dots (not commas).
1
2. Download text file economic_indicators.txt from http://www.efzg.unizg.hr
(Katedra/Statistika/Članovi/Doc.dr.sc. Josip Arnerić/Econometrics). You can save text file on your
desktop (hrv. radna povšina) if desktop is your working directory. Import dataframe into R from text file
using command:
mydata=read.table(file="economic indicators.txt",header=TRUE)
colnames(mydata)
Dataframe mydata contains time series from period 2000Q1 to 2014Q3 (59 quarters) with respect to
following economic indicators of China: exchange rate (CNY/USD), fdi_capital (FDI, capital used in
millions of CNY), gdp_current_price (GDP in current prices, billions of CNY), gdp_growth (in %),
ind_production (volume index on industrial production) and m2 (money supply M2 in billions of CNY).
3. Observations should be dated as regular frequency data using command:
mydata=ts(mydata,frequency=4,start=c(2000,1))
mydata
You can plot any time series using ts.plot command. If you want to plot GDP, only a third column of
mydata should be considered. Be aware that each column of mydata can be separated as a single
time series by referencing on the certain index in the square brackets [i,j]. Index i stands for the
rows, while index j stands for the columns.
ts.plot(mydata[,3],ylab="GDP in China",col="red")
4. Decompose time series of GDP in China using additive model. Plot all components.
components=decompose(mydata[,"gdp"],type="multiplicative")
plot(components)
5. Compute seasonal factors and explain the meaning of these factors for each quarter!
components$seasonal
Quarter
Q1
Q2
Q3
Q4
Seasonal factor
% change
2
6. Compute seasonally adjusted values of GDP (blue color) and compare them with actual values
(red color) on the single graph.
adjusted=mydata[,"gdp"]/components$seasonal
ts.plot(mydata[,3],adjusted,gpars=list(col=c("red","blue")),main="Seaso
naly adjusted and actual values of GDP)")
7. Estimate ACF coefficients for variable GDP growth up to 10 lags
ACF=acf(mydata[,"growth"],lag.max=10,type="correlation",plot=FALSE,dem
ean=FALSE)
ACF$acf
plot(ACF)
Lag
1
2
3
4
5
6
7
8
9
10
ACF
Note: The autocorrelation coefficients at all lags should be nearly zero if a time-series follows white
noise process and all Q-statistics (Ljung-Box) should be insignificant with large p-values
8. Check if GDP growth follows a white noise process using Ljung-Box test for lags 1, 5 and 10.
Box.test(mydata[,"growth"],lag=1,type="Ljung-Box")
Box.test(mydata[,"growth"],lag=5,type="Ljung-Box")
Box.test(mydata[,"growth"],lag=10,type="Ljung-Box")
Q1  ___________
Q5  ___________
p  value  ____________
p  value  ____________
Q10   ___________
p  value  ____________
9. Use the Augmented Dicky Fuller test (ADF) to test whether GDP growth (in the levels) is
stationarity or not at 1%, 5% and 10% level of significance.
adf.test(mydata[,"growth"],alternative="stationary")
10. Repeat ADF test to test whether GDP growth in the first differences is stationarity or not at 1%, 5%
and 10% level of significance.
differences=diff(mydata[,"growth"])
adf.test(differences,alternative="stationary")
Is there a unit-root? Should data be differenced?
3