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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: AW: Fitted probabilities using prvalue for logit model |

Date |
Fri, 16 Jul 2010 09:17:59 -0400 |

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 > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**AW: st: AW: Fitted probabilities using prvalue for logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**References**:**st: Fitted probabilities using prvalue for logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**st: AW: Fitted probabilities using prvalue for logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

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