Panel Data Models with Individual and Time Fixed Effects

An introduction to basic panel data econometrics. Also watch my video on "Fixed Effects vs Random Effects". As always, I am using R for data analysis, which is available for free at r-project.org
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Link to previous video: • Fixed Effects vs Rando...
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[@ramziabduljadayel4991]

I have been searching for several hours to understand those fixed effect. This video summarized it all in 30 minutes. Thank you very much.

[@amnest1ac]

I am truly thankful for your time putting together this lecture. The book I am reading doesn't show a single example of how fixed and time-fixed models work in practice. This has been enlightning. Thank you!

[@MrGotro1]

My goodness, thank you. I have been looking for a clear. transparent, concise, and intuitive explanation of panel data fixed effects models, and I finally found one, along with a perfect applicable and intuitively understandable example.

[@SirFwuz]

this guy teaches better than my 120C econometrics professor atm. makes perfect sense, more clear. good work!

[@ryanc7840]

This is the best lecture I have taken about the Fixed effect. You are better than any other econometrics professors I have met. Thank you so much for your crystal clear explanations.

[@oaholla3855]

Thanks so much for making the learning easier through practical application form the basics of panel data to actually generating the data in excel and thereafter running it in R. Also, your explanation was sequential from ordinary model to addition of fixed and time effect. Thank you

[@thehornyvegan1425]

you should be the benchmark for a standard lecturer employed for econometrics courses. very good video

[@soumyashreekar2519]

This is the best I have ever seen on KZbin. I have found the best stats teacher. Thank you so much.

[@BurkeyAcademy]

Thanks!

[@niharikasharma6556]

Havn't understood Fe and Re models better than this. Thanks

[@kumaransivan]

Thank you so much! You made it so simple to understand what they really means. I wish if commercially available econometrics books, going forward, included a simple videos like this one to explain the concepts in simple terms before they start throwing with vector/matrix calculus, how nice the world would have been!

[@ShelbyBryant]

You are a super great statistics teacher!

[@BurkeyAcademy]

Thanks for the compliment!

[@bastihome]

Really helpful video for my presentation on statistical methodology on friday. Thanks!

[@fatemehentezari2705]

Thank you so much. I was looking for a good tutorial to see what is going on in panel data. Your tutorial was the most useful one :)

[@prnaidu2157]

explained In such a simple way, it was really v.helpful.I'm new to econometrics but this was so helpful. Thanks a lot

[@latoni_ger1600]

Very helpful and easy to understand! Thank you!

[@BurkeyAcademy]

Thanks for the compliment, I really appreciate the support!

[@BurkeyAcademy]

Hi Ahmad- The short answer is: Yes, there is no problem if you have an unbalanced panel. The longer answer is... if there is a REASON why some observations are missing that is related to what you are explaining, it becomes a problem. For example, if you are explaining GDP, but some countries don't report GDP in bad years... then simple panel models aren't appropriate anymore. But, if data is missing for more or less random reasons, you are OK.

[@geetuchaudhary3083]

Very well explained with a small set of data, easy to comprehend conceptually and practically.

[@dr.anjalirane4034]

Simple & easy, yet the Best !!!

[@0cean27]

Thank you, this video is really helpful and easy to understand.

[@BurkeyAcademy]

I am not familiar with this kind of model, since I am not a Finance person. However, I am looking into it, and if I am able to understand it relatively quickly, I'll do it!

[@namirabenbo7251]

This is the most helpful video I have seen so far. Thanks a bunch, prof!

[@2freet]

Great video, I just start investigating into panel data and R, so this was just awesome!

[@Tomjrf]

Thank you very much, I wonder how would the interaction of person times test be treated.

[@smylieeeee]

Thank you so much for this video. I have a really basic question-I understand that R automatically calculates the error term so there's no need to input that, but where in R do you incorporate and/or calculate the intercept b0 part of the equation for panel data models?

[@BurkeyAcademy]

I don't understand the question... every stats package calculates the estimates for the y intercept and slopes for you. Often the y intercept is labeled "Constant" or "Intercept" as at 6:55 the intercept estimate is 33.0628. What do you mean by "incorporate"? I am also confused what you mean by "calculates the error term so there's no need to input that"-- where are you thinking of "inputting" it?

[@gemmay2k974]

Extremely helpful - thank you

[@kidikarus9106]

What I don't understand yet: Does RStudio include those dummy variables automatically or do you have to change the dataset manually i.e. adding the dummies 'by hand'? thank you for your answer

[@BurkeyAcademy]

If you have a variable containing words (e.g. Male, Female) the R will automatically create k-1 dummy variables. If the categorical variable is numeric (e.g. 1,2,3, 4 for freshman, sophomore...) then you can tell R to treat it as a nominal variable, or "factor". var1

[@thefedericap]

Could you please suggest me where I can find information about performing panel data analysis in SPSS? many thanks

[@linde_learn3892]

This is very good! Thanks so much

[@johannesmohr6333]

Thank you very much for your video! Do you have a literature source for the general equations, that you use, because I need it for my work, which I am writing at the moment? And can I use the same method with more than one independent variable?

[@nopenever5107]

Hello,sir.You said that whenever we put dummy variables into a model,we need to leave out one of them to avoid multicollinearity.Does the same go for time dummies? I mean,within a year range of 2004-2014 should I exclude dummy2004?

[@yanfengli4821]

Very detailed explanation. It's really helpful. Thanks.

[@kellysamakabadi7311]

Very helpful. Thank you

[@jimmylonexx1384]

Very good and simple viedeo, whats the difference beteween fixed and random effects model?

[@antrabhatt100]

Hi, is there any video on two period panel data. When and how it can be used.

[@pedrocolangelo2458]

Hi, Burkey! One more time I'd like to congratulate you for another great video. Would you mind if you indicate some books for someone who wants to learn these concepts in econometrics? Thanks!

[@BurkeyAcademy]

It is hard to know what level of a book to recommend- at the basic level I like A. H. Studenmund "Using Econometrics", at a higher level I like Wooldridge "Econometric Analysis of Cross Section and Panel Data"

[@pedrocolangelo2458]

@@BurkeyAcademy Thanks, Burkey! That was exactly what I was looking for

[@CrescentRollCarl]

Thanks for the clear and helpful explanation!

[@kalkito9419]

Very nice and clear. Thanks.

[@harumin4522]

what an awesome educational video. this really helped. thanks so much prof!

[@BurkeyAcademy]

I am so glad it helped!

[@zeetroo1]

hello sir, truly speaking this video helped a lot while handling the panel data. basically i have utilized the fixed effect for the purpose of estimation (individual and the time fixed both) and the whole procedure i followed was actually based upon the guide lines provided here in this video tutorial. I'm stuck at a point and hope by means of utilizing your vast experience within the handling panel data sets would provide me a batter guideline for the further proceedings. After conducting the process of estimation, the core concern laved upon the researcher is that to justify the empirical findings deduced from the estimation by means of solid interpretations to the model have estimated. Can you please help by providing an appropriate reasoning that might help interpreting the coefficients and the fundamentals of the model for the individual and time fixed effect models. Looking forward for your kindness

[@BurkeyAcademy]

+Zeeshan Bashir Interpreting the other coefficients (not the time or fixed effects) will be just as you normally would. Interpreting the individual fixed effects are cautioned against by many, though sometimes it might make sense. Keep in mind that individual fixed effects will be calculated using a dummy variable for each individual except one. So, each of these coefficients will be comparing the k-1 individuals to the one that does not have a dummy variable, the "reference individual". It tells you whether each individual, ceteris paribus, is higher than or lower than the reference individual. For time effects, these coefficients tell you how each period might be different from the omitted time period, normally t=1. Sometimes it is interesting to plot these time effects, as they might inform you about some interesting pattern (if you have lots of time periods).

[@TheManali10]

the concepts were explained really well. Thank you!

[@beatOmaniac]

one question if I have true zero values for the both for the dependent variable and the independent variable, does that has any affects when I would do the analysis?

[@BurkeyAcademy]

No, there is no reason why this would be a problem.

[@DoctorLuckyLuke]

Great video! Question: what happens if you want to have different coefficients for Studytime over the individuals? So that not only your intercepts but also the slope varies across your individuals.

[@BurkeyAcademy]

Then, similar to how we do the student effects, we would need to estimate a slope effect for each student. We do this by "interacting" the student dummy variable with the study time variable (i.e. multiply the 0/1 variable with the study time). If you like, I can do a quick video demonstrating this. Let me know!

[@DoctorLuckyLuke]

Thanks for your feedback. I would appreciate a short explanation on that along with the coding in R. (BTW I am using the plm package, which yields the same results. I guess the interaction terms should be coded the same way, no matter you use lm or plm.)

[@bezayeteklu928]

@@BurkeyAcademy Thank you so much for this video! Is the other video for the slope effect done? Can I get the link?

[@tiangeye7869]

Very helpful and great work! Thanks a lot!

[@idofsandeep]

Nice videos and good explanation. Thank you. plz correct website name in upcoming videos.

[@nishaadrao7584]

Great video! Question: What if you have an explanatory variable which has a constant difference over time - for example, age. Can the coefficient on this "age_it" variable be estimated consistently if we have both individual and time fixed effects? Say that everyone has a different age in the first time period, then can the "age effect" be estimated consistently?

[@BurkeyAcademy]

Nishaad Rao No, you can't throw in all of those variables at the same time, because the variable Age can be written as a linear combination of the person fixed effect and the time effect. This perfect multicollinearity will cause either the program to refuse to create estimates, or drop one of the dummy variables. There might be some tricks to get around this, but I can't say what they would be or how appropriate they would be. If you wanted to get a sense of how age might be related to the dependent variable, you could plot the individual fixed effects on the Y axis and the person's starting age in the dataset on the X. Though "officially" econometricians warn against taking this kind of thing seriously, nevertheless it could provide some insights.

[@t.nguyenminh9321]

thank you for the video

[@dagmawemenelek4973]

it is so interesting, but I have one question i.e. can we take time variable as months, weeks, or days rather than years.

[@BurkeyAcademy]

Yes, different "periods" can be any length of time.

[@sarahbb2144]

Thanks for the video. I'm a beginner with R. I have a question. I have 1100 observations and I followed the same steps in the video for summary statistics but it didn't work. What should I do? Thanks

[@BurkeyAcademy]

Without a WHOLE LOT more detail, I have no idea. Exactly what did not work?

[@ericwong8292]

Hello sir, can I know how do you get the data in 12:51?

[@stochasticNerd]

Would you please explain why the test scores change when you hit enter in Excel? You did give an explanation but I couldn't understand what you meant.

[@BurkeyAcademy]

Because I created the test scores as a function of the variables shown, plus random error. This random error gets recalculated by Excel when you hit enter. You can try this yourself by putting =rand() in a cell in Excel.

[@oleksandrlysenko1558]

Thank you very much for the videos, Sir. By taking FD or subtracting the value of our observation from the mean - we can avoid including dummies for student specific traits that affects test scores without reducing df. Do those methods simultaneously control for time effects or is there any similar method?

[@BurkeyAcademy]

1) If you are proposing subtracting each student's mean from their scores, then (it seems to me) you will be using up a degree of freedom each time you do this. You are using estimates from the data for each student to do the demeaning, which uses up a degree of freedom (whether or not your software knows and corrects the degrees of freedom for this). I could be wrong about this, but there is no "magical solution" to get around the mathematics of estimation and degrees of freedom. 2) Since I don't buy the "solution" in the first case, then no, there is not in the second either. But if anyone has a better idea than me with a reference, I'd love to hear more about this! I'll try to ask one of my Econometrics Guru friends to give me some insight here to make sure I am correct.

[@rifaatulhumaira1976]

Great videooo. this video is really helpful and easy to understand. thank youuu

[@SarveshwarInani]

Thanks a lot sir, This video was awesome and helped a lot in learning panel data in R in a very simple and time-effective manner. Keep posting sir... If possible, could you please upload some videos on Panel Vector Error Correction Model. Thanks again

[@BurkeyAcademy]

I use R for all of my econometrics.

[@MrGotro1]

So professor, if I'm interpreting this correctly, would it be safe to assume that because of some inherent characteristic that we may or may not even know, holding study time constant, before any student ever even picks up a book to study for this test, Joe and Sue are about 5 to 6 points better than Antonio, and Mark about 11 points better, which could be from innate ability or some other personal trait or characteristic, regardless of study time.... Would that be an accurate characterization? Thanks!

[@BurkeyAcademy]

I can't remember the exact numbers, but yes: whether it is innate ability, previous coursework, or whatever, we are trying to separate out the effect of studying from the individual-specific effects.

[@marhan2757]

It is everything fine with the word affect as it is a verb (effect is a noun) :-)

[@BurkeyAcademy]

I am not 100% certain how to model this correctly, but I am pretty sure that since companies entering and leaving the panel are not random, that you need to find a specific type of model that accounts for this. You should consult an expert on Panel Data Models, which I unfortunately, am not. Best of luck!

[@isabelanieto5694]

Hi, has anyone seen a numeric example with more than one independent variable? thanks

[@Abolwafa27]

Fascinating! Thank you very much.

[@rajansharma8270]

Thank you for the lecture. However, I am confused with one thing over here. You used 'lm' for doing all the panel data models. But as far as I've learned, we use 'plm' instead of 'lm' and also you didn't specify which model was fixed and which was random. For that, it would have been easier if you had specified. Such as for fixed effects, it would be: treg3_fe=plm(testgrade~study+studytime+testdrum, method="within")

[@BurkeyAcademy]

'plm' includes some fancy options, but they are not necessary when doing a basic fixed effects model with dummy variables. I did not use any random effects models in this video, that is in another video.

[@rajansharma8270]

Thanks for the explanation. That is probably we are doing 'linear' panel model right? I would also like to know about the way to test strict exogeneity assumption in any data (such as First difference method assumes strict exogeneity but how to ascertain this?).

[@sarahtaghvai-soroui6812]

very helpful - thanks very much

[@Joshua35070]

fantastic video, thanks for sharing your knowledge!

[@VijayKumar-ls8sq]

Hello, I have got three dimensional panel data of banks. I have dataset with countries, banks and year dimensions. I have got two countries and assigned them countries ID. Similarly, I have assigned ID to each bank. In panel regression (fixed effect) when I am assigning Index to "Country ID" and "Year", it is giving me an error. but when I am assigning index to "Bank ID" and "Year", it is working. I have made dummy for country but fixed effect model is omitting that dummy. Please help how to work with three dimensional data. Can I assign three indices to "Country", "Year" and "Bank"?

[@BurkeyAcademy]

No, because I assume each bank is not moving from country to country. Just a you cannot control for gender AND have person-specific effects, you cannot control for country and bank-specific effects. All of the influence of country is controlled for with the bank-effect.

[@nopenever5107]

Hello,sir.You said that whenever we put dummy variables into a model,we need to leave out one of them to avoid multicollinearity.Does the same go for time dummies?

[@BurkeyAcademy]

Yes! You have to omit one of the time categories.

[@nopenever5107]

Thank you for your reply

[@junkaizhao387]

Great help! Thank you.

[@noxkon1825]

Thanks a million

[@anitamahapatra5675]

Hello sir, Thanks for this well explained video. Actually I want to know what to do with the problem of multicolinearity after running the fixed effect model, except dropping the variables.

[@BurkeyAcademy]

anita mahapatra The big question is: What problem do you think multicollinearity is causing? Then, we can talk about ways to possibly address it.

[@anitamahapatra5675]

BurkeyAcademy Thank you sir for your response. Actually, my study in on Impact of SAARC(South Asian Association for Regional Cooperation) on non-members. To serve the purpose, I am trying to show the impact on the price level of the exported commodities to the region by non-members. Here non-members price will be depending upon the price that the members are charging for the same commodities and the tariff difference between the non-members and members. So here I have 2 explanatory variables for 17 years and when I run time fixed effect regression it is showing the problem of singularity.

[@BurkeyAcademy]

anita mahapatra OH, you have PERFECT multicollinearity. There is no choice but to drop something. Rather than using time fixed effects with 17 years, perhaps you could use a polynomial function of time (perhaps start with 4th or 5th order?) This might serve as an adequate control for time effects, perhaps not.

[@mohamedtrigui9488]

Thanks sir for this video, Please I have conducted an experience: the growth of bacteria over time after three different treatments we have tested three batch. Please I need to compare results by a mixed model regression analyses. I need to know my fixed effect and random effect

[@katarzynapuchalska8185]

thank you very much, it's really very helpful

[@BurkeyAcademy]

Watch my video on that! I have a link in the video to take you to it. Or, go to my channel page, or my web page. It is called "Fixed Effects vs Random Effects". It is likely to pop up on your screen as a suggestion after you watch this video, actually. If you have trouble finding it, email me and I can send you a link.

[@stosicsanja]

Is R shared for free and where I could get it? I am PhD studend and I need it for very simple analysis od Helth system?

[@KarolKarasiewicz]

Sir, first of all - sory for my English, I'll try my best. If You enter the test effect, it is not a different method than the Sutdent effect. But offect of the test is something like 'time series' - it's possible that there is correlation between results of test 1 and 2 and 3.... Effect of Student is indeependent (correlation cannot be computed or rather interpreted). If You enter these two effects in the same way - isn't it a mistake (for the test effect)? Shouldn't You have an intercorrelation between these tests in the model?

[@BurkeyAcademy]

Yes- go to my web site and watch my video on how to download it and where. It is called "Econometrics Preliminaries"

[@helloagain2101]

I have one question, for your model with fixed affects you assume a linear relationship btw hours studied and test scores, but what happens in the case that you study say 20 hours - in your model this would imply that you could theoretically get a score above 100%? How do you control for this?

[@BurkeyAcademy]

Good question- you would probably want to assume a non-linear form such as adding study time and study time squared. Another option if you want to definitely restrict the predicted values to be between 0 and 100 (or 0 and 1) would be to run a logistic regression. This is a bit different than OLS, but is designed to produce such a constraint.

[@helloagain2101]

Thank you!

[@abhimishraa]

Hi, If there is only one cross sectional variable (say country) with explanatory and dependent variables data for multiple years then should it be still considered a PANEL data or we can analyse it using multiple linear regression/OLS. Thanks.

[@BurkeyAcademy]

Abhijeet Mishra What you are describing is the typical country panel data set, say for estimating growth equations. So yes, this is a panel data set. However, just because something is a panel data set doesn't mean that "multiple linear regression" or OLS can't be used. OLS is just one of many estimation techniques, which might be appropriate, or might not be.

[@abhimishraa]

BurkeyAcademy​ thanks :) I ran the dataset with plm and lm, got same results both ways. My professor said I can consider it to be a normal dataset which I can analyse using either ols or Panel. Thanks.

[@BurkeyAcademy]

The statement ""either OLS or panel" doesn't make sense. It is like "either baking or bread". Panel is a kind of dataset. OLS is one kind of estimator. You have a panel dataset. Whether you use OLS, GLS, GMM, ML, Bayesian, etc. to estimate slopes is a separate question. The main concerns are getting the model structure you are estimating correct, and if you use OLS, that the assumptions are met.

[@abhimishraa]

BurkeyAcademy​ sorry for the poor choice of words. I wanted to say that I finally estimated the model using OLS. Checked for heteroscedasticity (using ncvTest() ) and milticolinearity, And it showed the results I was expecting. Thanks for the video.

[@BurkeyAcademy]

You are welcome! Just trying to clarify terminology if there was any need to! Cheers!

[@furkanoztanriseven8270]

Excellent! Thank you for this video!

[@VijayKumar-ls8sq]

I have nine year banking data. I want to use fixed affect model with 8 dummies for years. Can I use it, how will I interpret dummies for years. What model will be that 2nd in your presentation or the 3rd one.

[@BurkeyAcademy]

Neither one, I guess. Do you have multiple banks, or just one bank (or one collection of bank data)? If you have multiple banks you can use bank fixed effects. If you have multiple years, then you can just use time fixed effects, if you want.

[@VijayKumar-ls8sq]

BurkeyAcademy I am running two models. In model 1, I have got multiple banks with multiple years. In model 2, I have got multiple banks, multiple years and multiple countries. Can you please tell me which models from your video suit on each.

[@omarabukar8217]

Hellow Sir, how about if i use panel data in “Bilateral trade”...is it appropriate or not

[@BurkeyAcademy]

It seems like it can be done. See onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9396.00207

[@reajulchowdhury8534]

This is awsome! Really good

[@hlumisaxakaza3481]

Can someone please help explain this analysis on Stata ...

[@flank9800]

Thank you so much!

[@AnaGomez-tz1ht]

thanks a lot!

[@axelsantacruz4776]

Really Good videos, would you mind uploading a video doing a fama macbeth regression for a single asset? Thank you very much.

[@stosicsanja]

which program you used for this video?

[@sami-samim]

I learned R plus Econometrics :D

[@jianbin_jasonchen5802]

Dear All, I am a college student and currently encountering a stupid question. I am using a panel dataset to analyze how housing prices are determined. Everything was fine until I added fixed effects that caused almost all independent variables to be insignificant and the R-squared turns to be barely 0.2 and adjusted R is negative. My question is, is it necessary to use fixed effects when using panel dataset? Can I build up a consistent model without adding fixed effects? Sincerely, Jason

[@BurkeyAcademy]

JC: I assume your dataset's observations are houses that sold, yes? And, you probably have very few repeat sales... this means that when you add fixed effects you are adding a dummy variable for each individual house, so k (# of variables) is almost as large as n (sample size). What fixed effects does is control for the individual characteristics of that individual observation that are unchanging. Since ALL of the characteristics of a house will not change over time (size, # bedrooms, # bathrooms), there isn't enough variation left in your data for the model to figure out how these variables affect price. If my guesses about your data are correct, this is a case where fixed effects won't work. A similar example would be to take a panel data set of 30-60 year old and try to predict how education, race, degree type, and state of residence affect income. If you add a fixed effect for every individual, that controls out each person's individual characteristics that are not changing... so you can't tell the difference between "my" fixed effect on my income, and the part of my income determined by my race, education level, etc. Long story short; if you use fixed effects, only the characteristics of the houses that are varying over time can be estimated, and for most houses that will only be age.

[@jianbin_jasonchen5802]

Dear Sir, Thank you for your explanation and reply, I really appreciate that! I am sorry that I forgot to tell you that my dependent variable is housing sold prices in the 50 states of the U.S. in 3 different time periods (1980, 1990 & 2000) with total 150 obs, and the independent variables are economic factors such as income, sales tax; race factors such as Asian, Black, Hispanic; geographic factors such as north east, west, mid west, south east; social factors such as crimes; and so forth (not about the house characteristics like bedrooms). In my finest model (I assumed it is), it is TSLS model with R-squared 85%, and I also check its relevance and exogeneity, endogeneity, and so on. But when I added fixed effects to the TSLS model, most variables turn to be insignificant, and R-squared falls to 20%, I also used random effect, first difference, pooled OLS effect, but the results are similarly bad. The time fixed effects are not significant individually nor jointly. I also tried to add fixed effects to simple OLS model, but the results are terribly bad and abnormal. So I assume that the problem might be attributed to the wide time gap (10 years)? In this situation, can I rely on my TSLS model without fixed effects? Or, is it a problem to analyze panel dataset but not using fixed effects? Sincerely, Jason

[@zerohero109]

Can you explain fixed effect in difference in difference?

[@BurkeyAcademy]

This IS a video I need to make... I need to find a good example- perhaps one from a paper I did about rates of people on welfare might work. I have added this to the list, and will see if I can work on this sometime soon.

[@hemaangkotta7660]

can someone suggest me how to use panel data along with logit model.If my dependent variable is binary(1,0)

[@BurkeyAcademy]

Here is William Greene's presentation on the topic: pages.stern.nyu.edu/~wgreene/DiscreteChoice/Lectures/Part4-PanelDataBinaryChoiceModels.ppt

[@bayunugroho3232]

I use EViews and get "Near Singular Matrix: What should I do ? Thanks

[@BurkeyAcademy]

Eviews of no Eviews: You most likely have some variables that are perfectly collinear (even though the warning is saying nearly collinear). The simplest case is when you have a redundant variable, say one variable indicating 1 if females 0 otherwise, and another variable that is one for males and 0 otherwise. These two variables provide the same information, and are perfectly collinear (the male variable + female always =1). Sometimes it is harder to find; perhaps you have a dummy variable for each state, and also whether a state is in the south or not- these will also be perfectly collinear. You have some careful thinking to do to figure it out, but running VIFs or seeing which variables have extremely high standard errors, or looking at the variables Eviews refuses to compute slopes for can help point the way.

[@bayunugroho3232]

@@BurkeyAcademy thx, the problem is my dummy variable is my main independen varrible. I have total 6 independen variables included 2 dummies. I already try eliminated one of the dummies but it seem my main variable is the culprit. My sample is 84 manufacturing companies, can I do what you do ? I uae EViews btw. Thx

[@bayunugroho3232]

@@BurkeyAcademy can I group my manufacturing companies into Industries dummies ? For example I have 84 manufacturing companies and they belongs to 12 industries such as steel, textile and so on. Can I create dummies based on the industries, so there will be 12 new independen vatiables suc as Indust1 Indust2 and so on ? Thx

[@SOMRATKHONDOKER]

Please do a SYSTEM GMM

[@davidblane8318]

Could you explain why, when i do a fixed effects model, the regression analysis using lm produces a completely different r squared value when i use plm. And which model should I use? the one that provides a higher r square?

[@BurkeyAcademy]

If you are getting a different R^2, then you must unknowingly be running a different model (not including some of the controls). I had this problem when trying plm, and didn't keep playing with it long enough to figure out how to get it to work. Let me guess- you got a higher R^2 with lm? *Which to use? Well, the most important thing is that you know exactly what was estimated-- if there is any confusion, don't use that result. This is a fairly common problem with R package documentation-- sometimes it is difficult to figure out exactly what they are doing. R^2 (or even adjusted R^2) is no where close to the most important important thing in a model- doing things correctly in a way that makes sense is first and foremost.

[@davidblane8318]

+BurkeyAcademy my lm is producing a higher r squared but I'm including all the same values except in lm I'm using "+Country" but in Plm I'm using "index=c("country"). Is that what's causing my r squared to go so much higher? If so should I use the Plm model?

[@BurkeyAcademy]

If you need to use Country fixed effects, make sure they are being included. That is why I use lm, because I can see exactly what is happening. As I said, I had the same trouble with plm... including the index option did not actually seem to include these fixed effects in the model. Looking here (www.princeton.edu/~otorres/Panel101R.pdf) says you need to add the option model="within"

[@davidblane8318]

I've included LM: treg1=lm(IFDI~GAP+GDP+TRADE+POP+UNEM+GROWTH+TELE+INVEST+Country) R square = 86% PLM: test.fixed=plm(IFDI~GAP+GDP+TRADE+POP+UNEM+GROWTH+TELE+INVEST,data=testdat,index=c("Country","Year"),model="within") R square =30% i'm so confused how they report such different r squares despite using the correct function

[@davidblane8318]

It outputs the same coefficients however which is strange

[@BurkeyAcademy]

Oops, sorry for the typo!

[@razikridzuan4662]

hello Sir, just wondering one thing. I have 40 years data of fertility rate that do not depend on the same individual. Can I still use the longitudinal data analysis? the data look like below.. 15-19 20-24 25-29 ... 1970 25 57 101 ... 1971 24 56 99 ... . . . where 15-19 refers to the age of mother and 1970 refers to the year. Can this type of data be handled and explained by using longitudinal data analysis?

[@BurkeyAcademy]

+Razik Ridzuan I think it would be possible. People often look at countries, or states, or cities over time. Looking at age groups could also make sense. My only hesitation would be that there is a more or less deterministic progression of people through these age groups; i.e. the 20-24 year olds in 1970 will be the 25-29 year olds in 1975. So, I would try to think of a way to build that information into my models as well.

[@razikridzuan4662]

Thanks a lot. Can we make assumption that the people (age 15-19) observed on 1970, will be observed again when they are 20-24 years old?

[@BurkeyAcademy]

+Razik Ridzuan I don't know the source of your data, you'll have to look into that.

[@razikridzuan4662]

Okay.. Thanks for the help Sir.

[@BurkeyAcademy]

Happy to help!

[@bayusutikno5317]

sir, i wanna ask about unbalanced panel problem,,could you give me tutorial or website that i can solve the unbalanced panel problem ??

[@BurkeyAcademy]

This something that I do not know a lot about. Googling "unbalanced panel regression" produced some good PPT presentations on the theory. The plm package in R can handle some unbalanced panel methods.

[@bayusutikno5317]

Sir, i would like to ask about link to download eviews software as free license, where is that ? thank you very much.

[@BurkeyAcademy]

Eviews is not free, and other than that, I don't know much since I do not use it.

[@bayusutikno5317]

sir, if you don't use eviews to solve panel regression, what is the other software can solve panel regression ? and whether is that free for license use ? thank you very much.

[@BurkeyAcademy]

I use R for everything, as I did in this video, which is totally free. www.r-project.org.

[@punchline9131]

But where do I know or see that I have no unobserved heterogeneity when using dummy-variables? I don't really understand that. You still can say that Joe, Sue and Mark are better than Antonio because they are maybe smarter than him or whatever.

[@BurkeyAcademy]

Sure, but we are measuring that in the model. "Unobserved heterogeneity" is only a problem when we can't control for it. For example, if we had smarter people taking Test 2 than Test 1, we might think that test 2 is easier. But if we can control for that by repeated measurements, we can isolate the part due to the test, and the part due to the person, which is "observed heterogeneity". We observe it and measure it.

[@daryluzgaviria5513]

si tan solo se pudiera traducir a español seria buenisimo.

[@BurkeyAcademy]

Lo siento... hablo un poquito espanol, pero no tan mucho ensenar en espanol. Espero que puedes apprender de mi en ingles.

[@daryluzgaviria5513]

hay no se nada de ingles y me gustaria tanto entenderte xq para la clase de estadistica me toca exponer sobre este tema con ejemplo y todo y realmente no se como hacerlo y este ejemplo me parecio el mas claro.