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Stata – be the master

Stata – be the master. Stata. “After I have run my standard commands, what can I do to make my model better (and understand better what is going on)?”. Using dummies with interval variables can help improve fit. Create two extra dummies: one for here and one for here

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Stata – be the master

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  1. Stata – be the master Stata

  2. “After I have run my standard commands, what can I do to make my model better (and understand better what is going on)?”

  3. Using dummies with interval variables can help improve fit • Create two extra dummies: one for here and one for here • Or (typically when you have a lot of data points): • create dummies per group

  4. Variables need not be normally distributed … but it is often nice if they are (and gladder price will give you a graphical representation as well)

  5. interact.ado • A command to generate interaction effects • Centralizes automatically for interval variables (and that’s important) interact var1 var2, gen(var1_X_var2) Installation: + Download diagfiles.zip online + Put files in some folder + Add that folder to adopath (adopath + “/folderpath”) (+ Add this adopath statement to “profile.do”)

  6. Interpreting interactions:when you have interactions,“there are no main effects any more”

  7. Potential transformations - fracpoly … and there are several options, for instance to decide on the space of searched transformations

  8. fracplot shows the estimated shape

  9. Finding outliers - diag2.ado (but only possible after regress, and you have to keep thinking yourself!)

  10. The better way to find outliers in logit: ldfbeta(“finditldfbeta”)

  11. Note: Actually not completely Correct. Better (but more tedious), is to standardize the X-variables first.

  12. Other possibilities … • Try to find a subset of your data for which your model works better / differently (typically easier when you know something about the topic substantially) • Consider sequences of models, instead of focusing on “the best model”: 

  13. Sequences of models(easiest when you do not have that many variables)

  14. Handy bits of coding global VARS var1 var2 var3 … reg y $VARS forvaluesi = 1/10 { gen var`i’ = (varindata == `i’) }

  15. Granddad talking: More buttons get rid of determination … zebra

  16. squeeze, but be honest

  17. To Do • Back to your logistic regression assignment. • Compare what others have done with the dataset that you had. • Improve, squeeze, and deliver one assignment (make that a do-file) per data set

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