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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 11:01:46 -0400 |

I don't know what you mean by "determine the relative importance of my additional dummy variables relative to the benchmark model with its explanatory variables?" But in this case -prvalue- is obviously not working for you. How are you measuring "importance"? If you mean "significance", have you tested the joint significance of the two variables with -test-? (Adding variables will always increase the log-likelihood, so "improvement" is not a guide). If the criterion of importance is "predictive accuracy", then compare ROC curves for the two models with -roccomp-. Unfortunately, the ROCs for both models will be systematically optimistic, but the differences could still be revealing. For better accuracy, some kind of cross-validation approach is needed. For cross-validation approaches, see: http://www.stata.com/statalist/archive/2008-02/msg00686.html An unreferenced Stata program for cross-validation is contained in: http://www.mail-archive.com/r-help@r-project.org/msg82508.html There is also a literature on "proportional reduction in error" approaches, including partial r-squares. See: Agrestic, Analysis of Categorical Data, 2nd Ed (2002) Wiley, Chapter 6. Measures of r-square based on the log-likelihood are difficult to interpret (p. 227). A Google search will turn up many references. (By the way, -prvalue- is not an official Stata command. I presume it is user-written. Please, as the FAQ request, give references for all the non-Stata commands you use.) Steve On Fri, Jul 16, 2010 at 9:54 AM, Marc Michelsen <marcmichelsen@t-online.de> wrote: > 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 >> >> * >> * 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/ > > > * > * 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>

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

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

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