AW: st: AW: Fitted probabilities using prvalue for logit model

["Marc Michelsen" <marcmichelsen@t-online.de>]

Steve,

of course there are four possible combinations -- however, in my set-up
there are only three valid combinations. 1/1 is not possible.

Does your statement mean that -prvalue- is not an appropriate measure to
determine the relative importance of my additional dummy variables relative
to the benchmark model with its explanatory variables?

Marc

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Steve Samuels
Gesendet: Freitag, 16. Juli 2010 15:18
An: statalist@hsphsun2.harvard.edu
Betreff: Re: st: AW: Fitted probabilities using prvalue for logit model

There are four combinations of two dummy variables, not three, so your
statements don't make sense. The coefficients of the variables, if you
hold others constant,  refer only to the relative associations among
those four categories, not to any absolute levels.  Those are
determined by the values at which you fix the other covariates and by
the constant term. .It is well-known that prediction at the means of
covariates will not even reproduce the mean prediction, which in turn
is the raw  prevalence.  It is quite possible that all four
predictions could be lower than the crude prevalence rate. So, there's
no reason to expect those predictions to match those of any
"benchmark" model and a (single?) benchmark probability.

Steve



On Fri, Jul 16, 2010 at 6:02 AM, Marc Michelsen
<marcmichelsen@t-online.de> wrote:
> Dear all,
>
> as I didn't get an answer to my problem below, I am trying to rewrite the
> question more precisely/generally. The reference for the approach is the
> following: DeAngelo, H., L. DeAngelo, and R. M. Stulz. "Seasoned equity
> offerings, market timing, and the corporate lifecycle." Journal of
Financial
> Economics 95 (2009), 275-295. I am referring to the table on page 284.
>
> I am estimating the fitted probabilities of a logit model at fixed levels
of
> the explanatory variables using -prvalue-. I have a benchmark model and
> therefore also a benchmark probability of the event. Including my two
dummy
> variables in a second model specification (improves Peusdo-R2 and Chi2)
> actually lowers the probability of the event. However, the probability
> should increase if the dummy variables are coded 0 (dummy 1)/1 (dummy 2).
> The probabilities are lower in all three possible combinations of the two
> dummies. Although the coefficients of the logit model show the correct
signs
> and are statistically significant for one of the dummy variables.
>
> Does anybody has a view on this?
>
> Many thanks for considering this posting
>
> Marc
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Marc
Michelsen
> Gesendet: Donnerstag, 15. Juli 2010 11:12
> An: statalist@hsphsun2.harvard.edu
> Betreff: st: Fitted probabilities using prvalue for logit model
>
> Dear Statalist users,
>
> I am running a logit model to estimate the effect and relative importance
of
> market timing and rating concerns on the decision to conduct a seasoned
> equity offering (panel data).
>
> Including my rating concern proxy variables in the regressions improves
the
> fit of the logit model (Pseudo-R2 and Chi2) compared to the standard model
> (including only market timing and control variables). One of the two
rating
> concern proxies (positive rating momentum) is statistically significant at
> 5% with a marginal effect of -1.7%. The other one (negative rating
momentum)
> shows a positive marginal effect but has no significant influence.
>
> In order to gauge the relative importance of market timing versus rating
> concerns, I am trying to obtain predicted probabilities of conducting a
> seasoned equity offerings (SEO) in a given year. Therefore, I am using the
> "prvalue" command to calculate the probabilities at representative values
of
> the explanatory variables (control variables at sample means, good vs.
poor
> market timing opportunities). Neutral market timing opportunities
translates
> into a SEO probability of 5.2%, which is comparable to the study von
> DeAngelo/DeAngelo/Stulz (2009) p. 284. But if I measure the probabilities
> for positive, negative and neutral rating momentum (the other explanatory
> variables are set equal to the former model specification), the
> probabilities are always lower compared to the benchmark model (3.8% /
5.0%
> / 4.9%). While it is reasonable to assume that positive rating momentum
> lower the SEO probability, the results for the two other rating variables
> are surprising.
>
> Obviously, this weakens my hypothesis that rating concerns are one of the
> drivers of seasoned equity offerings.
>
> Does anybody have an idea why the fitted probabilities are lower in all
> three cases although the model fit is improved if I include the respective
> explanatory variables?
>
> Many thanks
> Marc
>
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-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

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