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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 * * 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/

**Follow-Ups**:**Re: st: AW: Fitted probabilities using prvalue for logit model***From:*Steve Samuels <sjsamuels@gmail.com>

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

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