In general, all my variables are time-variant. But I don't know how exactly that makes sense and is necessary. ,fe -? Here I could include the a continuous time variable with a count of the 340 match days (from 1 to 340, as I have 10 years with 34 matchdays each). Isn't that then the same as when I do -xtset team- beforehand and use -xtreg. This would mean that it would be sufficient to include i.team in the normal -regress- command. I got the book from the library and have thought a bit about my data: actually, nothing should change over the years/seasons, so I don't need the influence of the seasons (i would test that of course but it shouldn't change something). (For example, you might look for continous time trends rather than yearly idiosyncracies, or group time into somewhat longer intervals than one year.) Different people have different rules of thumb, but if you aren't going to have a minimum of 10 games per team per year, you are not in good shape to model all of those effects and you might have to rethink your approach. But you don't want your number of explanatory variables to outrun your sample size and leave you overfitting the noise. As I don't follow sports, I have no idea how many games a soccer team typically plays in a year.
There is also the question of the total sample size. At most, I might go with team as a level for a random effect, but keep i.year on the bottom level of the model. I would be more inclined to do a one-level model and just include i.team and i.year among the explanatory variables. While 22 teams is possibly a (barely) adequate sample of team-space to estimate team-level variation, 10 years is probably not. When you are using random-effects in multi-level models you are, in effect, sampling the universes of those effects to estimate outcome variance at that level. My concern is the lmited numbers of both teams and years. Looking at your data and the description, I wonder if a multi-level model is your best bet for incorporating team and year effects. Ken Chui gives excellent advice, and I, too, endorse that book. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. dataex- will save you time it is easier and quicker than typing out tables. Either way, run -help dataex- to read the simple instructions for using it.
Regress command stata install#
If not, run -ssc install dataex- to get it. If you are running version 17, 16 or a fully updated version 15.1 or 14.2, -dataex- is already part of your official Stata installation. If you do, then you should show the example data using Stata's -dataex- command. If you don't already have your data in Stata, then it is premature to ask for help with code. Even then, to make use of the example data, they would have to import it into Stata-and who knows what kind of subsequent cleaning it would require before it was usable.
Regress command stata download#
First, many of the people who respond here will not download anything from a stranger. The activity on this site is about Stata (or general questions about statistics.)Īlso, for future reference when asking Stata questions here, providing example data in a spreadsheet is not helpful.
For help with SPSS, you should go to an SPSS user forum.
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