Statalist The Stata Listserver


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

RES: st: RES: ratio between oprobit marginal effects


From   "Sergio Goldbaum" <sgoldbaum@fgvsp.br>
To   <statalist@hsphsun2.harvard.edu>
Subject   RES: st: RES: ratio between oprobit marginal effects
Date   Fri, 24 Mar 2006 19:39:36 -0300

Dear Maarten:
 
Thank you very much for your kind answer. I am right now studing your suggestion. 
Bedankt, 
Sergio Goldbaum
Fundacao Getulio Vargas, Sao Paulo
Brazil.
 
 

	-----Mensagem original----- 
	De: owner-statalist@hsphsun2.harvard.edu em nome de Maarten buis 
	Enviada: qui 23/3/2006 21:42 
	Para: statalist@hsphsun2.harvard.edu 
	Cc: 
	Assunto: Re: st: RES: ratio between oprobit marginal effects
	
	

	Sergio:
	-mfx- cannot be used to calculate the ratio of two marginal effects, you will have to derive the
	marginal effect yourself, and use -nlcom- to come up with the ratio and its standard error.
	
	Consider the example below. In this ordered probit the probability that a "mean car" falls in the
	lowest category of rep78 is the area under a standard normal curve left of -cut1 + xb, whereby xb
	is _b[price]*mean price + _b[mpg]*mean mpg + _b[foreign]*mean foreign. Let xb1 be -cut1 + xb.
	Similar xb2 is -cut2 + xb, and so on. Also let Phi be the cumulative density function of the
	normal distribution, and phi be the probability density function of the standard normal
	distribution. So Pr(rep78=1) = -Phi(xb1). The probability that a mean car falls in the second
	category is the area under the standard normal curve between xb2 and xb1, so Pr(rep78=2) =
	Phi(xb2)-Phi(xb1).
	
	The marginal effect of mpg on the probability that a car is in the lowest category is how much
	this probability for being in the lowest category changes for a unit change in mpg, i.e. the first
	derivative of -Phi(xb1) with respect to mpg. Using the chain rule you can see that that is
	-phi(xb1)*_b[mpg]. Notice that phi is *not* written with a capital letter, so refers to the
	probability density function which is the first derivative of the cumulative density function.
	Similarly the marginal effect of mpg on the probability to be in the second category is
	d(Phi(xb2)-Phi(xb1))/d mpg = phi(xb2)*_b[mpg] - phi(xb1)*_b[mpg]. These are shown in the example
	in the first two equations in the -nlcom- command. You can compare the results with the results
	from the -mfx- commands at the bottom of the example to check. All that is left to do is compute
	the ratio of the two, which is done in the third equation of the -nlcom- command.
	
	Note that the marginal effects are evaluated at the mean of all explanatory variables. In other
	words this is a marginal effect for a "typical" car, but not a car that acutally occured in the
	data, since no car is 30% foreign and 70% domestic. For this reason some people dislike using
	means of dummy variables (in our case foreign) and use the modal category instead. I have used the
	mean of foreign because you used the -mfx- command without specifying the -at()- option which
	means that you evaluated the marginal effects at the means.
	
	*-----------------begin example-------------------
	sysuse auto, clear
	recode rep78 2=1 3=2 4=3 5=4
	tab rep78
	oprobit rep78 price mpg foreign
	
	/*xb for domestic cars with mean price and mpg*/
	sum price if rep78~=. & price~=. & mpg~=. & foreign~=.
	local mp = r(mean)
	sum mpg if rep78~=. & price~=. & mpg~=. & foreign~=.
	local mm = r(mean)
	sum foreign if rep78~=. & price~=. & mpg~=. & foreign~=.
	local mf = r(mean)
	local xb1 -_b[cut1:_cons] + _b[price]*`mp' + _b[mpg]*`mm' + _b[foreign]*`mf'  
	local xb2 -_b[cut2:_cons] + _b[price]*`mp' + _b[mpg]*`mm' + _b[foreign]*`mf'  
	local xb3 -_b[cut3:_cons] + _b[price]*`mp' + _b[mpg]*`mm' + _b[foreign]*`mf'  
	
	nlcom (y2_mpg: - normden(`xb1')*_b[mpg]   )      /*
	  */  (y3_mpg:   normden(`xb2')*_b[mpg] -        /*
	            */   normden(`xb1')*_b[mpg]   )      /*
	  */  (y3_y2:   (normden(`xb2')*_b[mpg] -          /*
	            */   normden(`xb1')*_b[mpg]) /    /*
	              */  (-normden(`xb1')*_b[mpg] )) 
	 
	
	
	mfx compute, predict (outcome(1))
	mfx compute, predict (outcome(2))
	*------------------end example---------------------
	
	HTH,
	Maarten
	
	--- Sergio Goldbaum <sgoldbaum@fgvsp.br> wrote:
	> I am trying to adapt a model developed in a 1990 T. Bresnahan and P.
	> Reiss paper ("entry in monopoly markets") to Brazilian data.
	>
	> Following their model, I need to perform a ratio between marginal
	> effects after an ordered probit command.
	>
	> That is, after
	>
	> . oprobit y x1 x2 x3
	>
	> where y = 0,1,2
	>
	> I asked for the marginal effects
	>
	> . mfx compute, predict (outcome(1))
	>
	> . mfx compute, predict (outcome(2))
	>
	> which gave me, say, "dydx1(1)" and "dydx1(2)".
	>
	> Now, what I need to estimate is "dydx1(2)/dydx1(1)" with its standard
	> error.
	>
	> I guess I need to use "nlcom" command, but I have no clue on how to
	> write this last syntax statement, despite all my tries.
	>
	
	
	-----------------------------------------
	between 1/2/2006 and 31/3/2006 I will be
	visiting the UCLA, during this time the
	best way to reach me is by email
	
	Maarten L. Buis
	Department of Social Research Methodology
	Vrije Universiteit Amsterdam
	Boelelaan 1081
	1081 HV Amsterdam
	The Netherlands
	
	visiting adress:
	Buitenveldertselaan 3 (Metropolitan), room Z214
	
	ძ 20 5986715
	
	http://home.fsw.vu.nl/m.buis/
	-----------------------------------------
	
	
	       
	       
	               
	___________________________________________________________
	Yahoo! Messenger - NEW crystal clear PC to PC calling worldwide with voicemail http://uk.messenger.yahoo.com
	*
	*   For searches and help try:
	*   http://www.stata.com/support/faqs/res/findit.html
	*   http://www.stata.com/support/statalist/faq
	*   http://www.ats.ucla.edu/stat/stata/
	

<<winmail.dat>>




© Copyright 1996–2020 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index