Re: st: Several questions regarding xtprobit and margins command

[Nick Cox <njcoxstata@gmail.com>]

My guess is that, although the error message may be confusing you,
-Outcome- does not qualify as an acceptable argument for -margins-
because, as said, it does not qualify as a factor variable or
interaction.

Nick

On Tue, Nov 13, 2012 at 8:29 AM, Tobias Morville
<tobiasmorville@gmail.com> wrote:
> Hey Nick, and thanks for the anwser on Q#1, and the grammar correction too :-)
>
> I run
>
> -xtprobit stop_dummy Outcome outcome_lag1 seqEarn seqearn_outcome,re-
>
> Where seqEarn goes from 0 to approx 800 (in a very few obs). Only in
> integers. Outcome is the number of eyes the die shows, and
> seqearn_outcome is a interaction between seqEarn and Outcome.
>
> This adds another question.
>
> As seqEarn (their accumulated earnings they get every game round) is a
> positive monotonically increasing function of Outcome(t-z), is there
> anything wrong with NOT adding the interactionterm between them?
>

<snip>

>>> ________________________________________
>>> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Nick Cox [njcoxstata@gmail.com]
>>> Sent: 12 November 2012 18:20
>>> To: statalist@hsphsun2.harvard.edu
>>> Subject: Re: st: Several questions regarding xtprobit and margins command
>>>
>>> I'll comment on your problem #1.
>>>
>>> The help for -margins- starts
>>>
>>> "margins [marginlist] [if] [in] [weight] [, response_options options]
>>>
>>>     where marginlist is a list of factor variables or interactions that appear
>>>     in the current estimation results."
>>>
>>> When you give arguments immediately after the command, the crucial part is
>>>
>>> "margins marginlist ...
>>>
>>>     where marginlist is a list of factor variables or interactions that appear
>>>     in the current estimation results."
>>>
>>> So, it would help if you gave the exact and complete -xtprobit-
>>> command you used. I suspect that the error message will make sense
>>> when we see the exact model you fitted.
>>>
>>> Nick
>>>
>>> P.S. "dice" is a strange word even to those for whom English is a
>>> first language. "dice" is a plural: the singular is "die". One die,
>>> two dice.
>>>
>>> On Mon, Nov 12, 2012 at 2:58 PM, Tobias Morville
>>> <tobiasmorville@gmail.com> wrote:
>>>
>>>> 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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