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Re: st: Significance of Probit estimates versus marginal effects

From   Maarten buis <[email protected]>
To   [email protected]
Subject   Re: st: Significance of Probit estimates versus marginal effects
Date   Thu, 23 Oct 2008 08:45:31 +0100 (BST)

--- Kir1 <[email protected]> wrote:
> When i run a probit I find the variable of interest statistically
> significant. However, once I list the marginal effects using
> 'margeff', the variable is not statistically significant anymore.
> (margeff, at(mean) same as mfx is still significant as the probit
> estimate, albeit at a different level of significance).
> What can i make of this?Is the effect of the variable significant or
> not? what is the different meaning of the significance of the probit
> estimate versus that of the marginal effect?

These two tests test different hypotheses: The coefficient in a probit
model tells you the effect of a variable on the latent propencity for a
positive result, while while -margeff- and -mfx- will give you an
effect on the probability of a positive outcome. The effect on the
probability is usually the thing of interest, however the size of this
effect (and thus whether or not it is significant) depends on at what
values of the explanatory variables you evaluate that effect. In other
words each observation has it's own effect on the probability, which
depends on the values of all its explanatory variables. One of the
advantages of -margeff- over -mfx- is that -margeff- allows you to
compute the average effect rather than the effect at average values of
the explanatory variables. The distinction here is between the effect
for a typical individual and the effect of someone with typical values
on it's explanatory variables. However, because you have specified the 
-at(mean)- option you are using the same concept of effect as -mfx-.

So, what should you do? Choose which effect you are interested in, the
effect on the latent propensity (-probit-), the effect on the
probability for someone with typical values on the explanatory
variables (-mfx- or -margeff- with the -at(mean)- option), or the
effect on the probability for a typical person (-margeff- without the
-at(mean)- option). Choose your null hypothesis, typically that would
be that the effect of interest equals zero. Then report whether or not
you reject that hypothesis. (When we are saying that an effect is
significant, we are implicitly saying we went through all these steps
and concluded that we rejected the null hypothesis)

Hope this helps,

Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room N515

+31 20 5986715

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