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st: AW: Fitted probabilities using prvalue for logit model


From   "Marc Michelsen" <marcmichelsen@t-online.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: Fitted probabilities using prvalue for logit model
Date   Fri, 16 Jul 2010 12:02:21 +0200

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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