Aassalam o Alakum, Friends, hope u all doing good…

Today we will discuss about the ARLD model and preconditions of this Approach

using EVIEWS 9.Following is a chart which will clarify about the model section

on the base of data stationary.

In above chart ill focus on ARDL I have mention in above that we can Run ARDL

when we have our data stationary mix I.e. Few variables are stationary at level and

few ones at first difference but it’s also important to know that ARLD also can be

run if our variables are purely stationary at level or purely at first differences.

I have few assumptions for ARDL approach data

Data must be free from autocorrelation

Data must be free from heteroscedisty(HSK)

Data must be normal distributed

No one variable stationary at I(2)

Data should be stationery purely at level (o) or purely first difference (1) or

mixture of level and first difference.

Now lets us start applying ARDL using EVIEWS 9.

1. Step one drag you excel file on Eviews 9 icon

2. Check stationary level of your series if your series fulfill the

assumptions of ARDL then apply ARDL otherwise move for other

tests.

3. Now we have checked stationary level of our variables let suppose

variables are stationary on mixture pattern few ones stationary at level

and few stationary at first difference.

4. Go to quick ------------ select estimate equation and from bottom

and from drop down Manu select ARDL, when u will select ARDL

new window will be open.

From here I selected

ARDL option

The Specification tab gives you option for legs of variables in the above

screenshot we have two options for legs, first one is automatic and second is fixed,

1. if you select automatic leg then Eviews automatically chose appropriate leg

for your variables but you have to choose maximum leg for your variables.in

automatic leg selection option u can specify different leg length for

dependent variables and independent variable let suppose I can select 6 leg

for dependent and 8 for independent it’s all up to and next Eviews

automatically select what must be the suit able leg length for dependent and

independent variables.

2. But if we select fixed leg length then we must specify same leg length for

the both of dependent as well as independent variables.

3. List of fixed Regressor this option gives us opportunity to use all those

variables which are fixed or static variables, i.e. variables without legs.

4. Trend specification from this option u can add trend or trend and constant

or any static variable also can be specify

5. Option tab; you can chose the Akaike Information Criterion (AIC),(SC),

(HQ), or the Adjusted R-squared.

6. Dynamic selection in this area we write our equation dependent first and

then all independent variables.

From automatic

selection chose

maximum legs for

dependent and

independent

variables

You also can

chose fixed,,

List of fixed

Regressor

Option

tab

Dynamic

selection

Let suppose I write my equation and go-ahead.

I selected automatic leg lent criteria and used maximum 4 legs and all things remain unchanged because

which option I was required selected by default but you can change according to different situation, it’s

all depend u how much maximum leg u should include,, after this Eviews automatically select leg,,, I do

ok and proceed.

In above results we can see that we have use AIC for the optimal lag lengths

we see how AIC chose these legs ,we will check it with the help of graph, go

to views of above resulted window ---model summary—graph.

Here are results we can see , I

have include 4 legs but Eviews

automatically select 4 for

dependent variables , while

three for FDI independent

variables etc.

See the numbers of leg and same our

results are indicating that for dependent

variables 4 legs suitable while same for fdi ,

gdp and lie,, On these legs AIC is minimum

Now we have need F-STATISTICS value so that we can conform we can move

further or not,,,,

For F statistics we will go in above resulted windows views-----coefficient diagnostic-

--bound test

Conclusion from above results we can conclude that there is cointegression in our variables.

F-statistic value tells about the cointegression

among variables if F value comes less than

critical bound values then we conclude that

there is no cointegression among variables.

There is different critical value of bound on

different level of confidence; here our F value is

above from upper and lower bound test so we

can say there is cointegression in our variables.

Not if our F values come less than critical value

must do add or remove variables or adopt any

other way.

So we move on for long run relationship. Go to view of resulted window or

above window ----coefficients diagnostic----cointegression and long run form

Further we can check serial correlation, heteroscedisty

or normality of data etc.

Thank you so much for being with me…

Best of luck

Here we can see

long run relation

Note:coineq(-1)

must be negative

and significant,,