ADF unit root test using eviews econometrics

ADF unit root test using eviews econometrics

To check the existence of shocks present in a data by the help of Augmented dickey fuller unit root tests or ADF unit root test using eviews econometrics, you need to follow below step:

1. Open Eviews6.exe

2. Click on File -> Open -> Foreign Data as Workfile…

3. Open the data file “broadband_1 “ by selecting through the path
C:\Program Files\SPSSInc\Statistics17\Samples\English

4. Select MARKET_1 -> OK

5. Click on Quick -> Series Statistics -> Unit Root Test…

6. In Series Name write: MARKET_1, then click OK

7. Select test type as ADF -> OK

8. The output table is:

The above model (ADF unit root test using eviews econometrics) can be expressed by the following equation:

unit root testing – eviews interpretation

From the above model and the table, since t-statistics of β is 2.506 that is t>1.5, therefore this means that shocks are present in ∆M1. Since the coefficient of ∝ is equal to -0.0046, that is, ∝≠1 therefore shocks are not permanent in this model. If ∝ would have been equal to 1, then this means shocks are permanent.

Now we will check significance of the whole model with the help of R-Squared or Adjusted R-Squared. If there is only one independent variable in the model then R-Squared is used and if more than one independent variable, then we use Adjusted R-Squared. In this case, since we have more than one independent variable, therefore we will consider Adjusted R-Squared.

The value of Adjusted R-Squared is equal to 0.0825 * 100 = 8.25%. This means that this model is 8.25% healthy.

Now to check whether the above value of -Squared or Adjusted R-Squared is significant or not we will consider F-statistic or Prob (F-statistic) value. We will consider that value (F or P) which will be significant.

F-statistic or F > 3.84 is significant

P (F-statistic) or P < 0.05 is significant

Therefore in the above table F = 3.5 is insignificant, whereas P = 0.034 is significant.

Hence we can conclude that this model explains 8.25% which is proved by P statistics.

Lastly, the output model is generated by 3 methods:

1. Akaike info criterion                    87912443719463
2. Schwarz criterion                        98569907567117
3. Hannan-Quinn criter.                   92063741135591

The maximum value in the above methods is 11.98, therefore the report or result which is being generated above is represented by Schwarz criterion.

All of the above interpretation proves that this model has temporary shocks.