VAR Model in EViews|| Vector Autoregression Model in EViews || EViews Tutorials
ฝัง
- เผยแพร่เมื่อ 19 ก.ย. 2024
- Hello friends...
This video explains how to perform #VAR Model in #EViews.
The video also explains how to interpret the VAR Model results.
Video - Cointegration Tests Model 1: • Cointegration Test in ...
Video - Cointegration Tests Model 2: • Cointegration Test in ...
Video - Granger Causality Test: • Dumitrescu- Hurlin Pan...
Thanks for Subscribing!!
Happy Learning !!
Link to join telegram channel for queries and discussions: t.me/kshekhawat
![PURPEECH - นี่ฉันเองคนที่... (It's me) [Official MV]](http://i.ytimg.com/vi/NuhHd0hOOg8/mqdefault.jpg)
👌🏻👌🏻
Thank you Gagan 🤗
Very nice video 👍👍😊👌👌
@@achrajkanwar3998 Thank you Mommy 🙏
👌
Thank you Joban 🤗
Dear Komal
I appreciate your video and you perfect explanation. I learned more in this video than in my 4 years career.
I have a doubt. I am making a research and I have 2 variables- 1 of these is stationary at first difference and the other one is stationary at 2nd difference - Is still valid the VAR model ?
I would really appreciate your help.
Thank you so much for your kind words 😊 It is recommended that variables should be stationary either at level or at first difference. The application of various models become limited if variables are stationary at second difference. Try converting the variables in log and then check stationarity.
@@komalkanwarshekhawat_ what if the variable is stationary at first level after convert it into log, should we use the next progress in var with logarithmic variable? or we have to convert it as a normal variable?
Nice 😊 keep it up komal
Thank you Gagan 🤗
Dear Komal
Thanks a lot for your videos
You make our research more easier
My question is - i have 3 variables, 2 variables are stationary at first difference and one variable is stationary at second difference. Can is still run VAR/VECM
Thank you for your kind words. Regarding your query- all the variables should be stationary either at level or at first difference.
@@komalkanwarshekhawat_ Thanks a lot dear madam.
Please continue to do your service.
Thank You
@@sharmiladevij.c4890 Definitely. Thanks a lot 😊
👌👍
Thank you Gursewak 🤗
Maam please make a video on switching var in eviews
Sure 👍
Thank you so much for your reply. Please I request you to discuss tvpsvar in eviews
Ma'am can you please explain tvpsvar model in eviews
Dear @Komal Kanwar Shekhawat thanks for your effort. I have two questions: Where did 1.96 come from? why you comparing t-statistic with this number?
1.96 is the z statistics probability value at 5% level of significance. So you compare your results with the probability value at 5% level of significance.
1% and 10% level of significance is also used.
@@komalkanwarshekhawat_ thank you very much for your reply. So, we always comparing our t-statistic with 1.96 regardless the number of variables or there is a specific role for doing that?
@@islambabela5221 Always for 5% level of significance. For 1% and 10% the z statistics probability value is different.
😇👍👍
Thank you Amandeep 🤗
when we run VAR, should variables be differentiated or level data. I mean d(export) as you did. But many video, VAR run on level data. what should we exactly do?
Kindly follow the steps as explained in the video. Thanks.
👍
Thank you Anamika 🤗
Dear Komal, Can you please help me how to apply quantile VAR approach?????
Hello madam,I would like to find the long run relationship between variables, so do I have to apply var model,or is it ok to apply DOLS on my panel data for 10 years?
DOLS can be applied for estimation of the regression coefficients. But before that apply cointegration models to confirm the long run association.
th-cam.com/video/5Gcdb-_y5oE/w-d-xo.html
th-cam.com/video/sXm7hBMTu_c/w-d-xo.html
In both these videos I have explained cointegration models.
Hi there. Why after performing the VAR model, number of observations reduced into 47 instead of the total 50? Thanx
Due to consideration of lag difference.
Please share multivariate garch model video link
Sure, will upload a video soon