Saad Riaz commented> why you use max lag of 3? use 4 to 8 lags and than

apply this lag selection criteria....

Zia Eco Marwat commented> Use different lags ..... when u find the lowest AIC

value then that would be Ur proper lags.

Muhammad Anees commented> Commonly VAR with 4/5 will give you a

refernece. Changing the lag length in estimation of VAR will not affect the

model due to efficiency and parsimoniousness reasons. The model with * on

the AIC or BIC for the above two reasons can be selected. With more than 4

variables, select the model with BIC and less than 4 variables, use the model

with AIC.

Zia Eco Marwat comented> Sir here 0 lag has the smallest value... so now?

Olasehinde Timilehin commented> If you are to strictly follow this result.... It

means there was no sign of dynamic in the model. The best option is to use

OLS or any other GLS methods. Moreover... No lags implies long run. Please

read more on long run estimation.

Tringa Ymeraga commented> Thank you guys. In fact, I tried to use more lags

and the same thing happened till in the 6th lag. While, when I choose 7 lags,

the lag order is 7. Is this okay?

Sayed Hossain commented> 7 lags are not recommended. According to the

result, zero lag (as * is there) is the best option. In that case it is not dynamic in

nature. I would suggest to use simple regression model.

Moulana Naykrasyvishyy Cholovik There is no need to over-parameterize a

model with such small sample (29) by including 7 or more lags for yearly data.

Inclusion of too many lags than true lag length is same crime as exclusion of

lags from true lags. You can perform ‘VAR lag exclusion WALD test’ for

additional support to your results. See the IMF working paper on External

Shocks to Inflation in Sri Lanka by Duma, Nombulelo

Muhammad Anees commented> Including more than 3 lags with a model for

more than 4 variables and 40 observations creates efficiency issues. How do

people believe not adding more than 4 variables to a time series of 40

observations while we get a model of 7 or 10 variables. Please check the

comment of Moulana Naykrasyvishyy Cholovik in addition to one of my reply to

your questions.

expressing each endogenous variable in the model in terms of past values of

itself and other variables. Hence, VAR models essentially include lagged

values of all model variables. But choosing the appropriate 'lag length' is the

real problem. How can we choose the appropriate 'lag length'? In this case,

'data frequency' comes before even 'lag selection' criteria, as we can't even

utilize the 'lag selection' criteria to choose the most appropriate 'lag length'

before estimating a VAR model. Depending on whether the data frequency are

annual, quarterly, or monthly helps us to estimate the initial VAR model to start

with. In case of quarterly and monthly data, we need to use 4 and 12 lags

respectively, to estimate the initial VAR models. Then, how about the annual

data? In this case, the researcher should use his or her intuition. As in most

cases, we're not blessed with long enough annual observations, we should

use a lag length, which is neither too small nor too big, since lags are

important, as they eliminate residual autocorrelation in VAR models. The

question is, if the lag length is optimal or not. Lag length criteria indicate a

definite way of selecting the optimal lag after estimating the initial VAR model.

Under the guideline of 'lag length criteria', we need to use the 'lag length',

which is selected by most of the 'lag selection criteria' named after the

econometricians who developed them, like HQ, SIC, AIC and LR, etc.

Generally, we choose the lag length for which the values of most of these lag

length criteria are minimized, indicated by asterisks in the EViews output. But

the selection of optimal lag length does not stop there. We need to check the

residual autocorrelation of the estimated VAR model, at least for the chosen

lag length. Furthermore, due to multiple lags, VAR models are

overparameterized and lags erode the degrees of freedom and weakens the

strength of 'diagnostic tests'. That's why, we need to perform the 'lag exclusion'

tests to select the minimum lags that eliminates VAR residual autocorrelation.

VECM is rigorous than that of VAR. My valuable advice is to select a lag that

will make the model stable.... and the other assumptions will not be violated...

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Meo School of Research

Shishir Shakya

Noman Arshed

Hossain Academy Note

Univariate Models |

Multivariate Models |