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PANEL COINTEGRATION DOLS

For panel cointegrated regression models, the asymptotic properties of the estimators of the

regression coefficients and the associated statistical tests are different from those of the time

series cointegration regression models. Some of these differences have become apparent in

recent works by Kao and Chiang (2000), Phillips and Moon (1999) and Pedroni (2000, 2004).

The panel cointegration models are directed at studying questions that surround long-run

economic relationships typically encountered in macroeconomic and financial data. Such a long-

run relationship is often predicted by economic theory and it is then of central interest to estimate

the regression coefficients and test whether they satisfy theoretical restrictions. Chen, McCoskey

and Kao (1999) investigated the finite sample proprieties of the OLS estimator the t-statistic, the

bias-corrected OLS estimator, and the bias-corrected t-statistic. They found that the bias-

corrected OLS estimator does not improve over the OLS estimator in general. The

Results of Chen et al. suggested that alternatives, such as the fully modified (FM) estimator or

Dynamic OLS (DOLS) estimator, may be more promising in cointegreted panel regressions

(BALTAGI)

Phillips and Moon (1999) and Pedroni (2000) proposed an FM estimator, which can be seen as

a generalization of Phillips and Hansen (1990). Recently, Kao and Chiang (2000) proposed an

Alternative approach based on a panel dynamic least squares (DOLS) estimator, which builds

upon the work of Saikkonen (1991) and Stock and Watson (1993).

Kao and Chiang also investigated the finite sample properties of the OLS, FM and DOLS

estimators. They found that (i) the OLS estimator has a non-negligible bias in finite samples,

(ii) the FM estimator does not improve over the OLS estimator in general, and (iii) the DOLS

Estimator may be more promising than OLS or FM estimators in estimating the cointegrated

panel regressions((BALTAGI ECONOMATRIC ANALYSIS OF PANAL DATA.).

HOW TO TEST PANEL COINTEGRESSION USING EVIEWS

PRECONIDTIONS

1. Variables must be stationary on same order, like X is stationary at order 1st then Y also

should be sationarity at order 1st

2. If variable are cointegreted then we apply DOLS model otherwise not

3. We apply DOLS for panel data

4. For long run relationship

So here is data file let suppose my al variables are stationary at first difference, so next step is to

check cointegration in variables,

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Remember I have check the sationarity of my data and now I want to know about the cointegration in

variables

Steps === go to quick===group statistic===cointegration test

When you will enter cointegression following window will open, and write the name of all your variables in

the blow box and ok

Here i have my variables, there are

few steps for panel cointegration,

the first step is, to check the

sationarity of data, if the entire

variables are cointegreted in same

order then we can employ

cointegration.

Steps === go to

quick===group

statistic===cointegr

ation test

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When you will press ok following window will be open

Here I wrote my dependent

variable first and then all

independent variables. press

ok

Here we have three test

types for checking

cointegration: find from

drop down button’

1: pedoroni

2: kao

3: fisher

You can use all the test

type all are beneficial

Let suppose I select

pedroni, and pedroni have

three deterministic trend

specification

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Here in the above pic, we have three test types for checking cointegration: find from drop down button’

1: pedoroni

2: kao

3: fisher

You can use all the test type all are beneficial

Let suppose I select pedroni, and pedroni have three deterministic trend specifications, you can use any

specification if you select pedroni test type and other things remain same (advance users can chose

optimal lags, informational criteria etc) I select pedroni and individual intercept and ok

And these are the results of cointegration using pedroni and individual intercept

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In above results we have 11 probability values, and with the help of these values we can make

decision about the cointegration, null hypothesis for cointegration is that no cointegration, so

what is decisional criteria for accepting or rejecting null hypothesis?

If the majority of these values we find significant then we reject null hypothesis and accept

alternative hypothesis which is there is cointegration among variables. Or simple if majority of

these values we find less than five percent then we conclude that there is cointegration like in

the give results I have total 11 values and I have 7 values significant means majority of values is

significant so we can say there is cointegration ,

NOTE:I have confirmed that there is cointegration but for double check ill run again

cointegration test but this time ill include individual intercept and individual trend and for

further conformation using pedroni ill run again cointegration but involve no intercept no

trend and compare results of these there shapes ,, remember using individual intercept I have

confirmed that there is cointegration but for mind satisfaction we use other these two options,

you also can use Kao etc.

If you are using pedroni test and in pedroni test if individual intercept and no intercept and

trend are telling that there is cointegration but one shape is tell that is individual intercept and

individual intercept that there is no cointegration ,, here we can conclude that there is

cointegration because majority of shapes telling cointegration.

Here we have 11 probability values, and with the help of these values we can

make decision about the cointegration, null hypothesis for cointegration is that

no cointegration, so what is decisional criteria for accepting or rejecting null

hypothesis?

If the majority of these values we find significant then we reject null hypothesis

and accept alternative hypothesis which is there is cointegration among variables.

Or simple if majority of these values we find less than five percent then we

conclude that there is cointegration like in the given results I have total 11 values

and I have 7 values significant means majority of values is significant so we can

say there is cointegration ,

NOTE:I have confirmed that there is cointegration but for double check ill run

again cointegration test but this time ill include individual intercept and individual

trend and for further conformation using pedroni ill run again cointegration but

involve no intercept no trend and compare results of these there shapes ,,

remember using individual intercept I have confirmed that there is cointegration

but for mind satisfaction we use other these two options,

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OTHER METHOD OF KNOWING ABOUT

COINTEGRATION (KAO)

Now I select kao rather than pedroni and ok results are

blow

Quick, group

statistics,

Johnson

cointegration

and then I

select Kao ok

Here we can see null hypothesis

is no cointegration, but our

probability value is less than 5%

, means we will reject null

hypothesis and we will accept

alternative hypothesis , means

there is cointegration .

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Now I have found that there is a cointegration and now next step is to know about long run

relationship for this I ll run panel DOLS MODEL

Steps == quick = estimation equation,

Go to quick, estimate

equation, write

dependent variable

first and then all

independent

variables, and form

method select

cointeg- Cointegrating

regression

And when you will

select cointeg—

Cointegrating

regression another

window will open

which is following

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So when you will press ok a resulted window will be open which is for long run relationship and this will

be window of DOLS model make decision on the base of probability value.

From here I selected