Second Generation Panel Unit Root Test || EViews Tutorials || CADF || CIPS || PANIC
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- Опубликовано: 7 сен 2024
- Second Generation Panel Unit Root Test
This video explains how to run the Second Generation Panel Unit Root Test or check Stationarity of a series in E Views for a Panel Data in case of Cross Sectional Dependence.
1. Firstly, the video explains how to find out whether the Panel Data set is cross sectionally dependent or not.
Watch this video also on Cross Sectional Dependence Test in EViews-
• Unit Root Test in E Vi...
2. Secondly, the video explains the significance of the Second Generation Panel Unit Root Test.
3. Thirdly, the video explains step by step how to run the Second Generation Panel Unit Root Test in EViews.
The video explains all the test under Panel Data viz:
1. BAI and Ng- PANIC
2. CADF
3. CIPS
- Finally, the video explains how to interpret the results for the Second Generation Panel Unit Root Test obtained in E Views.
Link to join telegram channel: t.me/kshekhawat
To learn how to arrange Panel data in Excel file before importing in E Views watch this video- • Panel Data| How to arr...
To learn how to obtain descriptive statistics of a Panel data in E Views watch this video- • How to Interpret Descr...
Thanks for watching!
Happy Learning :)
#paneldata #unitrootrest #stationary #eviews #eviewstutorial #paneldataanalysis
#secondgenerationpanelunitroottest
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Ma'am, I guess this was a mistake at 10.35 onwards. You said since p0.05, yes we do not reject H0, meaning that in the case of stationarity, we would ACCEPT H0 (not H1), that is, data has a unit root or is non-stationary. I think you need to clarify this, as it will lead to a lot of confusion for others watching this video.
You are correct.
Insightful. Thank you for sharing
I'm having an error message that says automatic lag selection encountered an error most likely due to insufficient observation... Working on 10 years across 60 firms
Cross sections are too large in number. Pls increase the time span.
Thank you for your favor and reply, I have installed Eviews 12 and this option was shown to me. but my data is an unbalanced panel, and Eviews gave an error that this test (panel unit root with cross-sectional dependence) cannot be performed for an unbalanced panel. Please introduce me to the link of the video where you have taught the unit root test test (the second generation of the unit root test, with cross-sectional dependence) for the unbalanced panel. I looked for it myself, but I didn't find anything, thanks. My emphasis is on the unbalanced panel.
One of your great videos. What if CADF shows different p-values for different cross sections as showed in your example? How to interpret the result of CADF here! Thanx
Hello and thank you for your scientific and valuable content, I need this answer urgently. thanks. At minute 5:51, when your mouse is on the unit root test, a small black triangle can be seen on the right side of the unit root, which shows two separate parts:
1. cross-sectionally dependent
2. cross-sectionally independent
But for me, in my eviwes it is not displayed like that, and two options
1. cross sectionally dependent
2. cross-sectionally independent
Is it not displayed !!
My Eviews is version 11, what is the cause of this problem?
This option is available in Eviews 12.
@@komalkanwarshekhawat_
Thank you for your favor and reply, I have installed Eviews 12 and this option was shown to me. but my data is an unbalanced panel, and Eviews gave an error that this test (panel unit root with cross-sectional dependence) cannot be performed for an unbalanced panel. Please introduce me to the link of the video where you have taught the unit root test test (the second generation of the unit root test, with cross-sectional dependence) for the unbalanced panel. I looked for it myself, but I didn't find anything, thanks. My emphasis is on the unbalanced panel.
Great video, Thanks so mush 😊
i have a question, when using CIPS and CADF tests how can i check for the 1 difference and 2 difference ? i dont see the option to do it in eviews
Thanks for your kind words.
Second generation panel unit root test option is available in EViews 12 and new versions only. However, To the best of my knowledge, I couldn't locate this aspect in EViews 12. Still, You can perform first and second difference for CADF and CIPS in Stata. Good day!
Great video, thanks very much. What does it mean if my data shows no cross-sectional dependence when I don't include the trend and constant, but if I include them, then it shows very strong cross-sectional dependence?
Then there is a presence of cross-sectional dependence.
Excellent video, could you tell us wich version of Eviews you are using ? can you explain how to apply 2nd generation panel unit root test in stata? plis
Thank you! I'm using EViews SV 12.
I am not getting the cips and panic test in the unit root tests window . only adf and other tests
Try running again. The options must be there.
Here p value is greater than 0.05, so how can we accept the alternative hypothesis (10.40-11 time of streaming) pls. Check it. As per my knowledge we have to accept the null hypothesis.
Dear, the interpretation is that-
This last table provides the result of the Pooled version of individual ADF test statistic in the previous table.
Due to very high value we cannot reject the Null Hypothesis.
We cannot reject the Null Hypothesis that- All of the variables are simultaneously not cointegrated.
Thanx Thanx Thanx
Your welcome 😊
Thank you so much, i have a question (how to run the first and second difference for CADF and CIPS tests?)
Second generation panel unit root test option is available in EViews 12 and new versions. However, To the best of my knowledge, I couldn't locate this aspect in EViews 12. Still, You can perform first and second difference for CADF and CIPS in Stata. Good day!
So you say that if the test does not satisfy with constant and then go with trend? Ok leaving it aside and then you forgot about specifyig at level and first difference. You missed the lag selection process and also the results what you found with your data is not relevant what you said !!!
mam please clear me that if the p value is more than .05 that means the variable is stationary in second genration unit root test?
No, it doesn't. If p value is greater than 0.05, this means that the variable is not stationary at 5% level of significance. However if the p value is more than 0.05 but less than 0.10, you can state that the variable is stationary at 10% level of significance. But if it is more than .10, the variable is not stationary.
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@@komalkanwarshekhawat_ thanKyOu ma'am. if the data is not getting stationary in second unit root test can't we run 1st difference and second difference on second generation unit root test? or we can conclude that the data is not getting stationary at level and assume to be stationary at 1st differnece and 2nd difference after converting into log form?
@@VarneshghildiyalvibhuYou are interpreting it in the wrong way. The second generation unit root test is performed when there is presence of cross-sectional dependence.
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I have a question plz. If i regress the series on its own trend and found it significant on trend and constant. Does this indicate that i must choose the constant and trend option in this case? Thanks
Yes!
in CADF test , what if some of the cross sections exhibit non stationary? how can we interpret the result here for this specific variable? Thank you
Check whether it is stationary at first difference or not ?
@@komalkanwarshekhawat_ my data exhibits cross sectional dependence, so i used the 2nd generation unit root tests CIPS and CADF to test stationary using Eviews. CADF is giving me values/ pvalues for each cross section. How to interpret the results?
thank you so much
Welcome! Keep following 👍
thanks mam, i am following the steps and i am getting this error message "Automatic lag selection encountered an error most likely due to
insufficient observations." what may i do mam
The number of observations are very few and maybe you are taking more lags. Hence the error - insufficient observations.
Meaning, the observations are so less after automatic lag selection that the analysis cannot be performed.
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Very nice
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Please your explanation is contradictory
Kindly elaborate!
If we do not reject the null hypothesis, it means we accept the null hypothesis and reject the alternative hypothesis.