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st: Several questions regarding xtprobit and margins command


From   Tobias Morville <tobiasmorville@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Several questions regarding xtprobit and margins command
Date   Mon, 12 Nov 2012 15:58:21 +0100

Hi everyone, my first post at StataList, so be nice :)

I have a set of questions regarding the margins command, and marginal
effects in general.

I have a unbalanced paneldataset of 4124 observations, unevenly
distributed on 18 subjects.

My model is as follows: P(stop) = Outcome outcome_lag1 seqEarn, which
im estimating in a RE probit setting with xtprobit command.

Outcome: Outcome of a dice in period t. Lies from 1 to 6
Outcome_lag1: Outcome of the dice in period t-1
seqEarn: Accumulated earnings over each game. Drops to 0 if subject
chooses to stop, or the dice shows a one. Starts at zero, and can only
get more positive as people climb the reward ladder.

All of these regressors are significant.

Sooo, now the questions begin:

1) If i use -margins Outcome- (followed this guide
http://www.stata.com/stata12/margins-plots/), then i get this
errormessage: "'Outcome' not found in list of covariates", and that
actually is the case for all margins commands, and is my number one
headache.

The only marginscommand that works, is if i use the -margins,
dydx(Outcome outcome_lag1 seqEarn)-, which leads me to my next
problem:

2) When i use a -margins, dydx(Outcome outcome_lag1 seqEarn)- my
marginal effects are exactly the same as my regression coefficients?

If i change the code to -margins, dydx(Outcome outcome_lag1 seqEarn)
atmeans- they're the same again..?
(So APE = MEM?)

Im really confused about this, and i've read the ealier post
(http://www.stata.com/statalist/archive/2009-11/msg01517.html), which
covers some of the questions i have, but dosen't really anwser them.

If i use mfx compute, predict(pu0) they change, but they become very
small. And im guess that pu0 means that Im setting the random effects
slope to 9, which is a bad idea for my data, as there is quite alot of
random variability.

3) If i choose to ignore the fact that my marginal effects are the
same as my probit regression coefficients, then im in my next pickle.
That my marginal effects are constant. If i plot the predicted
probability of stopping the game, over seqEarn, its constant, which
suits my data very badly. And im afraid that i've misunderstood
something very basic.

if i try -margins, dydx(seqEarn) at (Outcome=(2 3 4 5 6)) vsquish-
marginal effects are the same over Outcome size...

.. and it's basically the same.

What i would idealy like to see, is that predicted probability changes
both over seqEarn and over Outcome and outcome_lag1 in some systematic
way, but right now my newbieness in Stata problemshooting is driving
me up the wall.

------

Background (just for the interested):

I'm currently working with a dataset of 18 subjects, playing a virtual
dicegame for 25 mins while in a fMRI scanner. The dice is random
(1-6), and if you roll one, you lose whatever you accumulated this
round. It's a balloon kind of thing: How far do people dare to go up
the exponential reward ladder, before banking their earnings.
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