Walter Enders. University of Alabama. This version of the guide is for student users of RATS and EVIEWS Cointegration and Error-Correction Models. Cointegration and Error Correction. Mechanism (ECM) Some Economic Applications Summary and Conclusions Exercises
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It says syntax error. Aoa Thank you for a very helpful post indeeed What about short run results? Dear Professor, with hope you are fine, My name is Feiyun Xiang. Prof, what if we have two exogenous variables but we are interested in testing long-run and short-run asymmetry for one exogenous variable, and we are interested in testing only short-run asymmetry for another exogenous variable?
Can we do these tests in one equation? Hi professor, I use your way and get every result but I ma wonder how to get the ecm coefficient? Follow borneotemplates. The raw data used to demonstrate with Eviews can be downloaded from google drive. Download Workfile. Or send a hi to my Email: hssanhanif gmail. This file contains the yearly data of inflation rate and ''percentage of food import to total import'' of India. The food model has the following form:.
So, Follow these steps: F. Step 1. P erform unit root tests to make sure that non of the variables are I 2. Step 2. Step 3. Step 4. Step 5. Step1: run unit root test to make sure that data are not I 2. If it is less than 0.
If p-value is less than 0. And if it is more than 0. I have checked each series and found that Foodt is integrated of order 1 or I 1 and inflation is found to be the integrated of order zero I 0. You can verify the order of integration with the Eviews workfile i provided in first paragraph.
Step 2 Calculate partial sum of positive and negative change:. To do this, copy each line of the commands given in the box paste it into the command area of Eviews then press ENTER in keyboard. To understand these commands , what these commands to is that they create first difference variable of inf and food: dinf and dfood, then create a series for the values of dinf which are not negative and another series for negatives.
Then they calculate the cumulative sum of each after multiplying positive and negative series by the first difference. The second box contains the set of differenced variables upto 4 lags. By specifying -1 to -4 , all the lagged terms of dfood from first lag to fourth lag are included. So as for the rest. The 0 lag of Xt, for example, is the Xt itself. Note here that the 0 lag is not included in case of dfood because dfood is already acting as dependet variable in the model. Choose unidirectional, backwards, p-values, set p-value 0.
I chose 0. Also choose uni directional and backwards. However, this setting is not hard and fast rule. The goal is to select the appropriate model specification with appropriate lags for the differenced regressors. You can choose other criteria to choose appropriate lags. By a quick glance to the output we can learn that Eviews removed some lags and that 0.
But they are not the long run coefficient. I said percent point because the variables are rates clearly, the food import response more to negative change because the coefficient is larger. Step 4: Asymmetric Cointegration test:. Before drawing any conclusion regarding the estimated coefficients one needs to check if variables are co-inetgrated. The coefficients would be sporious if variables are not cointegrated. For testing cointegration under NARDL, Shin at al recommended to use joint null hypothesis of level non-diffrenced variables and to compare the critical values of bound testing in pesran et al You can download the paper from internet, and find the table in Page If the calculated F statistics is found to be the greater than the upper critical value then there is evidence of co-integration.
And if not, then evidence of cointegration is not found. Basically, this joint null hypothesis acts as the null hypothesis of no cointegration. The procedure is illustrated in the picture below. After clicking OK you will see Wald test results as shown in the picture below. The calculated F-statistics for this asymmetric cointegration test. The calculated F-statistics is Here is the screenshot.
Of case III. In the table, k denotes the number of long run regressors. The reason is that, the variables actually came from from 1 variable, hence, the k lies between 1 and 2. Also notice in table that the critical value for small k is larger. If the null hypothesis of no cointegration is rejected by critical value of its smaller k, then according to shin et al there is a strong evidence of co-integration.
Decision: since The calculated F statisics is larger than The crticical value 7. Testing the presence of asymmetry:. But are they really statically different? If they are equal then threre is no asymmetry and if they are not then threre is evidence of asymmetry. Recall from step 3. So, To test the long run asymmetry in Eviews click on wald test and write this command below in the restriction box of wald-test.
Eviews will show the wald-test results as in the picture below. Decision: Clearly, the null of hypothesis of equality is rejected as p-value is less than 0. Wald test indicates that there is asymmetry in the long run impact of inflation on food import in India. The Last step :. Check the robustness by various diagnostic tests.
I will not explain theme here but I saved the results of what I have shown above including the results of diagnostic tests. This is because any shock to the system in the short run quickly adjusts to the long-run. Consequently, only the long run model should be estimated using OLS where variables are neither lagged nor differenced. It is the static form of the model. In essence, the estimation of short run model is not necessary if series are I 0.
Scenario 2: When series are stationary in first differences. Under this scenario, the series are assumed to be non-stationary but became stationary after first difference. One special feature of this is that they are of the same order of integration. Under this scenario, the model in question is not entirely useless although the variables are unpredictable. To verify further the relevance of the model, there is need to test for cointegration.
That is, can we assume a long run relationship in the model despite the fact that the series are drifting apart or trending either upward or downward? There are however, two prominent cointegration tests for I I series in the literature. They are Engle-Granger cointegration test and Johansen Cointegration test. The Engle-Granger test is meant for single equation model while Johansen cointegration test is considered when dealing with multiple equations.
If there is cointegration:. Implies that the series in question are related and therefore can be combined in a linear fashion. That is, even if there are shocks in the short run, which may affect movement in the individual series, they would converge with time in the long run. Estimate both long-run and short-run models.
If there is no cointegration:. Johansen Cointegration Test in EViews. The hypothesis is stated as:. H 0 : no cointegrating equation. Note: Cointegration test should be performed on the level form of the variables and not on their first difference. It is okay to also use the log-transformation of the raw variables, as I have done in this example.
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