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RE: st: Average marginal effects in ordered probit models

From   "Chris Ambrey" <>
To   <>
Subject   RE: st: Average marginal effects in ordered probit models
Date   Thu, 13 Sep 2012 21:05:00 +1000

Dear Maarten,

Firstly thank you for your prompt reply. Sorry about the references this is
my first time posting on the STATA list. The full references are below.

Karaca-Mandic, P., Norton, E., Dowd, B., 2012. Interaction terms in
nonlinear models. Health Services Research 47, 255-274.
Long, J., Freese, J., 2006. Regression Models for Categorical Dependent
Variables Using Stata, 2nd Edition ed. Stata Press, Texas.

I will try the ordered logit and log odds ratio approach. Thanks again for
your help.

Kindest Regards,

Christopher Ambrey


-----Original Message-----
[] On Behalf Of Maarten Buis
Sent: Thursday, 13 September 2012 7:16 PM
Subject: Re: st: Average marginal effects in ordered probit models

On Thu, Sep 13, 2012 at 9:31 AM, Chris Ambrey wrote:
<snip>. However, I want to use predictnl and/or nlcom
> to obtain the average marginal effects for several interaction terms 
> in the one model. I'm estimating an ordered probit model. I'm using 
> version 11.2 of STATA. Life satisfaction is my dependent variable 
> (0-10) and I have greenspace (continuous, percentage of greenspace in 
> the census tract) and a number of socio-economic and demographic 
> characteristics as explanatory variables (some dichotomous and others

> Furthermore, I have found a very recent article "Interaction Terms in 
> Nonlinear Models" by Karaca-Mandic, Norton and Dowd (2012) which has 
> been very helpful.

On Statalist we ask you to provide full references. See:

> Additionally, do the average marginal effects apply to a particular 
> outcome, such as a self-reported life satisfaction score of 10 and are 
> thus interpreted as the probability of reporting say a 10 for a one 
> unit increase in the explanatory variable?

To start with your last question: Yes. So, by estimating marginal effects
you have undone the very advantage of ordered regression. The point of
ordered regression is that you can use the ordered nature of the dependent
variable to get one effect for each (or at least some of
the) explanatory/independent/right-hand-side/x-variable. This simplifies the
model a lot, and simplifying is what a model is supposed to do but it also
involves a risk in the sense that the constraint could be wrong. If you undo
the advantage of an ordered model, by presenting the results in such a way
that you need to present effects for each category in the
explained/dependent/left-hand-side/y-variable, than there is not much point
in incurring the risk without the advantage, and you can just as well do a
multinomial logit/probit.

However, I would not give up so easily. Instead I recommend you use an
ordered logit and interpret the results in terms of odds ratios. You can
find an explanation on how to do that in

Hope this helps,

Maarten L. Buis
Reichpietschufer 50
10785 Berlin
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