Johanson Cointegration Test and ECM

7/23/2019 Johanson Cointegration Test and ECM

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Muhammad Saeed Meo, superior university Page

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Johansson cointegration, ECM

C0integration test (first we check stationary of data if all variable are stationary at first difference, then

we will check cointegration ,if we find cointegration then we can go for ECM ,following chart also

describing this procedure

Remember, the cointegration test is only valid if you have non-stationary series!

The purpose of the cointegration test is to determine whether several non-stationary time

series are cointegrated or not. The presence of a Cointegrating relation forms the basis of the

ECM(Cointegrating tells about the long run relationship, existence)

To perform Johansen cointegration test, first open the series: or quick-group statistics

Suppose all the variables are stationary at first difference and now we are going for

cointegration


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Simple enter and in a dialog box simple write name of variable like lgdp lexport and enter.

When you will inter a window will be open like

Explanation of this dialog box

Deterministic trend assumption of test Practical guides:

use case 1 only if you know that allseries have zero mean (unusual in empirical studies);

case 5 may provide a good fit in-sample but will produce implausible forecasts out-of-

sample.;

use case 2 if none of the series appear to have a trend;

use case 3 if series are trending and you believe all trends are stochastic;

use case 4 if series are trending and you believe some of them are trend stationary;

use case 6 if you are not certain which trend assumption to use (Eviews will help you

determine the choice of the trend assumption).

Now we select 3 and enter and results are these.


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The first two tables report results for testing the number of Cointegrating relations. Two types of test

statistics are reported: trace statistics and the maximum eigenvalue statistics. For each table, the first

column is the number of Cointegrating relations under the null hypothesis, the second column is the

ordered eigenvalues of the matrix , the third column is the test statistic, and the last two columns are

the 5% critical values

ECM error correction model (in above pages we saw cointegration among variable so now

we will go for Ecm

Why we use error correction model?

There are some problems in first difference these are following thats why we apply ECM.

In first difference even we find that spurious relation(meaningless relation,mathematically relation exist but logically does not exist like relationship between

GDP and Corruption there must be negative relation but due to trend here positive

relation exist) does not exist but we eliminate constant

Out correlation occur in error term(autocorrelation leads insignificant effects)

How to know either here is

cointegration of not.so read this

line and this will show how much

cointegration equations exist,

remember at least, there must beone cointegration equation exist in

this example we have two

cointegration equations that is

good. Sometime trace test

indication cointegration, while

max-eigen dontshow

cointegration between variables,

so not so serious prob, in this case

you can take trace test as bench

mark and conclude that there is acointegration. But it is better both

of test show cointegration.


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We can do over estimate or under estimate due to first differencing

Difficult to find static equilibrium

How to apply ECM?

Drag file on Eviews note if you are using eveiw 5 then save excel file in file type

2003( suppose I have checked already stationary level and cointegration )

Now youre variable appear in eveiw then simple go to quick estimate equation.

How to write equation of ECM simple first dependent variable then independent like

d(lgdp) c d(lexport) lgdp(-1) lexport(-1)and enter. Here GDP is our dependent variable and

export is independent all are in logged form and space in c and variables .(pre-condition I have

already told in crux part means all variables must be stationary at first difference)


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Now explanation of ECM

d indicate short run relationship between variable like in this table export and dgp having no

significant relationship in short run value.91 and in long run .94 probability value is value that is

more than 5% which is insignificant so no relationship in long run .the variables which have

values(-1) indicate that how much quickly in extraordinary situations dependent variable convert

D tells about the short run relation and prob value tell about the significant

or insignificant relation,, and coefficient tell about the positive or negative

relationship means if let suppose D LEXPORTS coefficient values comes in

negative and probability value comes less than five percent then we will

explain that there is short run negative and significant association between

exp and gdp . Same explanation for positive association and (-1) tells about

long run relation

Here u can conclude that in short run and long run explored dont have

relationship with gdp

R square values tells,, explanatory power of u model,, means how much ur

dependent variable is effecting due to independent variable like in this case

our dependent variable is effecting 99 percent .you note results are telling

that no relationship but R-square show much strength the reason is that Ihave not chosen good statistical test because all variables are not stationary

at first difference. I just apply this model for your understanding.

F-STATISTIS, values tells about the good fit of model. Means either jointly all

variables effect or not dependent variables .F-statics value must be more

than F-STATISTSITCS problity vlaue.(note selection of right statistical test is

most important to avoid from spurious results)

Durbin Watson this value tellsabout the auto correlation in ur

variables which is not good, if ur

values comes near 2 then there is

no correlation,, and D-W values

comes between 2-4(near two

means 1.6,7,8,9,) but much less

means harmful like in above results


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toward own actual level normally values comes equal to one or less than one if values increase

more than one it means dependent variable will not come again in actual level.

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Johanson Cointegration Test and ECM