DOLS Model

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 longrun 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 biascorrected 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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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.

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

Steps === go to quick===group statistic===cointegr ation test

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

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Here I wrote my dependent variable first and then all independent variables. press ok

When you will press ok following window will be open

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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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,

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. 5

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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From here I selected

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.

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