Re: st: Several questions regarding xtprobit and margins command

[Tobias Morville <tobiasmorville@gmail.com>]

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?

best,
Tobias



2012/11/12 Nick Cox <njcoxstata@gmail.com>:
> At least, if you are replying to me, know by reading the whole thread
> that I was replying to Tobias.
>
> Nick
>
> On Mon, Nov 12, 2012 at 7:48 PM, Tim Burnett (ECO)
> <Tim.Burnett@uea.ac.uk> wrote:
>> Oh, I realise I omitted some commas in the dydx margins commands.
>>
>> Sorry,
>>
>> Tim
>> --
>>
>> Tim Burnett
>> PhD Researcher
>> Department of Economics & ESRC Centre for Competition Policy
>> University of East Anglia
>>
>> Email: tim.burnett@uea.ac.uk
>> Phone: +44 (0) 7793 116 522
>> Skype: timb318
>> ________________________________________
>> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Tim Burnett (ECO) [Tim.Burnett@uea.ac.uk]
>> Sent: 12 November 2012 19:43
>> To: statalist@hsphsun2.harvard.edu
>> Subject: RE: st: Several questions regarding xtprobit and margins command
>>
>> The model is:
>>
>> probit  Y ib2.D i.supplier##i.S  i.B##i.supplier i.B##i.S  i.S#c.N  i.supplier#c.N  ib1.income ib1.employment i.children i.gender ib1.education ib2.age
>>
>> Where N takes the value 1,2,3 or 4, and B takes the value 0 or 1.
>>
>> margins, dydx(B) at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=1 D=3  income=4  employment=1  children=0 gender=0 age=3 education=1)
>>
>> gives dS/dB, and...
>>
>> margins, dydx(N) at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) B=1 D=3  income=4  employment=1  children=0 gender=0 age=3 education=1)
>>
>> gives dS/dN
>>
>> and for all possible S*supplier combinations:
>>
>> dS/dB + dS/dN = [margins, at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=2 B=1 D=3  income=4  employment=1  children=0 gender=0 age=3 education=1)] - [margins, at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=1 B=0 D=3  income=4  employment=1  children=0 gender=0 age=3 education=1)]
>>
>> But, the problem I have is in finding the standard error associated with the combined (dS/dB + dS/dN) term and I was wondering whether Stata has some way of calculating the standard error associated with the compound effect of a marginal change in several variables.
>>
>> I hope this makes sense now!
>>
>> Many thanks for your help!
>>
>> Tim
>>
>> --
>>
>> Tim Burnett
>> PhD Researcher
>> Department of Economics & ESRC Centre for Competition Policy
>> University of East Anglia
>>
>> Email: tim.burnett@uea.ac.uk
>> Phone: +44 (0) 7793 116 522
>> Skype: timb318
>> ________________________________________
>> 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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