COMPUTER EXERCISES 3
Import file „Economic indicators.wf1“ to EViews (download the file from the www.efzg.hr... and save it
on your desktop). This workfile contains time series data 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). Observations are dated as regular frequency data using commands „Proc -> Structure/Resize
Current Page... -> Dated-regulary frequency -> Frequency: Quarterly -> Start date: 2000Q1 -> End date:
2016Q4 -> OK -> OK“. Range of observations is extended for the out of sample observations
(observations in the sample + observations out of sample).
For extended range of observations create new variables:
 3 seasonal dummy variables (dummy variable for the second quarter is generated using „Quick ->
Generate series… -> Enter equation: d2=@seas(2) -> OK“… repeat commands for the third and the
fourth quarter ->…. d3=@seas(3) ->… d4=@seas(2). Note: seasonal dummy variable for the first quarter
is omitted).
 variable „time“ (use „Quick -> Generate series… -> Enter equation: t=@trend -> OK“).
 variable „time squared” (use „Quick -> Generate series… -> Enter equation: t2=t*t -> OK“).
 “time dummy” variable (use the Genrate series dialog box and enter equation: „d1=@recode(t>21,1,0)“
-> this means that the value will be equal to 0 for the first 22 observations up to 2005Q3 and for all
other observations the value will be equal to 1)
1. Estimate two following econometric models:
a)
ŷt  ˆ 0  ˆ1t
b)
ŷt  ˆ 0  ˆ1d1  ˆ 2 d1  t 
where y  exchange rate (CNY/USD), t  variable time ( t  0, 1, 2, 3, ...,58 ) and d1  0 before
2005Q3 and 1 after 2005Q3 (click on the Estimate button from Equation toolbar and specify equation by
list of variables for model a): exchange c t and for model b): exchange c d1 d1*t). Compare the actual
values and fitted values on the same graph within two estimated models (click on the Resids button
from Equation toolbar). Which model fits data better according to R square? Explain the meaning of
estimated slope coefficient after 2005Q3. Is slope coefficient after 2005Q3 statistically significant?
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(perform t-test and F-test using linear restriction C(2)+C(3)=0 -> click on the View button from Equation
toolbar and choose Coefficient Tests - > Wald – Coefficient Restrictions…)
Model a) ŷt  ________________________  R 2  ______
Model b) ŷt  ________________________  R 2  ______
Slope coefficient after 2005Q3 equals to ___________
H 0 ...
H1 ...
t  ___________  F  _____________  p  value : ____________ .
2. Estimate a model with a constant term (intercept), power trend and seasonal dummy variables to
describe the behavior of the GDP in China, current prices (from Quick menu choose Estimate
Eqution dialog box and specify equation by list of variables: gdp_current_price c t t2 d2 d3 d4).
ŷt  _______________________________________________________
Explain the meaning of estimated coefficients for dummy variables! Are dummy variables statistically
significant? Compare the actual values and fitted values on the same graph (click on the Resids button
from Equation toolbar). Calculate forecast values based on estimated model in range from 2000Q1 to
2016Q4 (click on the Forecast button and rename forecast values as gdp_forecast). Compare actual and
forecast value of GDP on the same graph (select both variables and right click -> Open as Group -> View
-> Graph… -> Single graph -> OK).
3. Estimate a same model again with a constant term, power trend and seasonal dummy variables and
perform following diagnostic tests to check if:
a) Residuals are normally distributed (View -> Residuals Diagnostics -> Histogram-Normality test)
Write down the null and the alternative hypothesis, the value of JB test and the p-value. According
to which distribution the test statistic is distributed?
H 0 ...
H1 ...

JB  ______ 

6


24
2
 3 
  _______


p  value  __________
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b) Residuals are mutually independent (View -> Residuals Diagnostics -> Serial Correlation LM test->
Lags to include: 2 -> OK).
Write down the estimated test equation, the value of LM test and the p-value.
 t  ____________________________________________________________
LM  ______ _______  ______________ ;
p  value  _______________
H 0 ...
H1 ...
If the null hypothesis is true then there is no serial correlation up to order two (number of lags is two by
default). Can we reject the null hypothesis?
Calculate the approximate value of the first order autocorrelation coefficient of residuals using DurbinWatson statistic? What can you conclude based on the value of the DW statistic in this example?
DW  _____________ ;
ˆ 1  1 
2
 ________________
c) Residuals have constant variance (View -> Residuals Diagnostics -> Heteroskedasticity test-> Test
type: BPG -> OK). Repeat the same test using White specification without cross terms (View ->
Residuals Diagnostics -> Heteroskedasticity test-> Test type: White -> do not include white cross
terms -> OK).
Write down the estimated test equation, the value of LM test and the p-value.
 t2  ____________________________________________________________
LM  ______ _______  ______________ ;
p  value  _______________
H 0 ...
H1 ...
If the null hypothesis is true then there is no heteroskedasticity of residuls. Can we reject the null
hypothesis?
d) If heteroskedasticity problem exist estimate the same model using robust standard errors, i.e. from
Estimate Eqution dialog box click Options and choose HAC (Newey-West) consistent standard
errors).
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