(Stata13): Tips to Building Panel Data in Stata #paneldata #longdata #categoricaldata #dummies

Why do you want to perform panel data analysis? Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.) to explore the behaviour of the groups in the sample with respect to a variable the groups have some semblance of commonness the groups have varying heterogeneities (i.e. differences) with respect to a variable perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why!  I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data”) (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”) (3) group classification (to explore the heterogeneities in the data) (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

Follow up with soft-notes and updates from CrunchEconometrix:

[CrunchEconometrix]

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[mohamedeltayeb]

Thank you so much. I am doing research on linguistics and this was very helpful. As a Sudanese, I am also very glad to see an intelligent African woman teaching the world about advanced econometric techniques.

[BrianJasonPonce]

This is by far the most helpful video on how to format panel data. Thank you!

[horveysenyosylvester]

Hello,
From South Africa, I just finished watching all the nine videos. It is the best teaching I have received on GMM. Cheers!

[lazharlabed]

good morning
Thanks for the great video, I benefited a lot from it
I built a model and got these results



Arellano-Bond test for AR(1) in first differences: z = -4.69 Pr > z = 0.000
Arellano-Bond test for AR(2) in first differences: z = -0.54 Pr > z = 0.589

Sargan test of overid. restrictions: chi2(61) = 100.02 Prob > chi2 = 0.001
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(61) = 64.61 Prob > chi2 = 0.352
(Robust, but weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(51) = 53.11 Prob > chi2 = 0.393
Difference (null H = exogenous): chi2(10) = 11.50 Prob > chi2 = 0.320
iv(x4)
Hansen test excluding group: chi2(60) = 64.57 Prob > chi2 = 0.320
Difference (null H = exogenous): chi2(1) = 0.04 Prob > chi2 = 0.842


Can the form be accepted or rejected?
Thank you

[mohamedlamroony]

Thanks alot dr, absolultly amazing explaining

[monzamana]

# respect from Tunisia
Great job, your videos are very helpful and your explanations are understandable. Thanks

[nowrinjahan]

Thank you soo much... Your videos are very very helpful.

[evan]

Tips to Building Panel Data in Stata
1. reshape wide to long stata
2. group classification (industry)
3. group classification
4. 4:30 categorise outcome variable
a. Get variable in place then use the xtset
5. Create dummy variables

[asgharkamal]

its was very fruitful video for my research study coz i have 64 obs, then create a dummy thank madam without your support its impossible for us to complete our PhD.

[wanjadouglas]

You are just amazing and saved the world

[christopherschroder]

This is so so helpful thank you very much! I would recommend maybe to do a video how to merge different data sets, especially panel data sets (with some data sets having more countries and years than others, which need to be dropped). This is what I have to do right now and I couldn't find any video for this. All the best

[rebeccagramiscelli]

Thank you for the help, from Brasil! =)

[ovwwrxw]

aoa.. I have been your viewer and completed my phd by learning your ARDL in eviews and kindly guide me how i can use your data of [[(Stata13): Tips to Building Panel Data in Stata]]]

[wanjadouglas]

Very helpful. Is it possible to get this raw data and the dofile for practice?

[rgportocarero]

Dear Madam,

Again, a very helpful video, thank you. My dataset looks at mean scores of companies from 2009 until 2020, not unlike your data (except companies instead of countries).

Company Year Score
X 2009 70
...
X 2020 80
Y 2009 60
...
X 2020 65

I am looking at the statistical significance in score increases over the years of the aggregate sample (e.g mean of 2009 scores to 2010 over all companies).

In your case: the question would be to calculate the Levene's test on equality of mean between yearly score increases/decreases of trade.

My question: I need to perform a Levene's test of equal means (sdt test x == sdtest y) over the covered years to test for equal variance difference. Therefore, I will probably need to perform a test of each year against one another. When I declare data to be panel data by xtset scores years, it gives the error of repeating time values in the sample.

I was wondering how to tackle this problem so that when I perform the test is still takes the mean of each year.

Much much appreciated!!

[vivien]

You've really helped me with understanding STATA in total! Thank you so much! I just wanted to ask, is there a video on how to classify data in Excel that I can use to help me?

[enongenebetrand]

Amazing video Dr. save me alot of time. deeply appreciate. But i have an issue on how to classify the data in excel interms of regions and income level. Any hint on how to classify panel data in excel?

[basanijamelahoveni]

Very useful video, what does it mean you have to reset to historic background in order to create Dummy variables?

[meerrasiq]

Respected mam, I just need a little clarification
Mam as far as my little knowledge goes, the inequality data (gini) you are using in this video is not available in a balanced form. There are a lot of missing values ( data) in between the years for the majority of the Countries whether you refer to World Bank or Solt. But your data shows no missing values and for all the years data is available. How mam?? When gini data is not available for all 141 countries without missing values.