Autoregressive distributed lag model by Vector autoregression

Autoregressive distributed lag model

The above model contains ARDL (autoregressive distributed lag model) in addition to VAR / vector autoregression because of both variable, independent and dependent.

The above model is also another form of ARDL model (autoregressive distributed lag model) because AR process is also their and similarly Lag distribution of the dependent variable is there as well.

Investigating ARLD (autoregressive distributed lag model model) through VAR (vector autoregression) in EViews:

In order to investigate ARLD model by the help of vector autoregression in Eviews, you need to follow bellow steps:

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 and MARKET_2 -> OK

5. Click on Quick -> Estimate VAR…

6. First write the dependent variable and then independent variable. In our case, MARKET_1 is dependent variable, whereas MARKET_2 is independent variable, then Click OK:

7. The output shows a table with t-statistic value and coefficients. Since we have selected MARKET_1 as our dependent variable, therefore we will use the value in MARKET_1 column.

Mathematically, the above vector autoregression model can be expressed as:

From the above expression of β1 we can define that, if previous lag of M1 increases by 1, then the current lag of M1 will increase by 1.206. We define β2, β3 & β4 in the similar manner.

To be significant the value of t-stats should be greater than 1.5, therefore from the above model we can see that M1 (t-1) is significantly predicting M1 (t) but M1 (t-2) is not significant. Since the dependent variable is significantly being predicted by one of its lag, therefore AR process executes. On the other hand, the t-stats of M2 (t-1) & M2 (t-2) both are insignificant; therefore LD process cannot be proved in this model.

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 0.998 * 100 = 98.8%. This means that this model is 98.8% healthy.

Now to check whether the above value of -Squared or Adjusted R-Squared is significant or not we will consider F-statistic value.

F-statistic or F > 3.84 is significant

Therefore in the above table F = 10926.4 are insignificant.

Hence we can conclude that this model explains 98.9% which is proved by F statistics.

Lastly, the output model is generated by 2 methods:

1. Akaike AIC 81048631603165
2. Schwarz SC 98811071349254

The maximum value in the above methods is 11.988, therefore the report or result which is being generated above is represented by Schwarz criterion.

Finally, we can conclude that this model is an ARLD model, but only AR process has been proved in the model.