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From |
Nahla Betelmal <nahlaib@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: interpretation for negative and positive slope combination of interaction term |

Date |
Thu, 9 May 2013 20:51:21 +0100 |

Thanks for the reply David, but I think there is something not quite right. If you check this file, p.133 figure 7.8 http://www.sagepub.com/upm-data/21120_Chapter_7.pdf You will notice that in order to get the slope for the group with Dummy= 1 (overconfident manager in my case), we should add the coefficient of beta and gama ( MV and OC*MV in my case) , and to get the intercept for those managers we should add the alpha and y ( my model intercept and the coefficient of OC ) I found the same in other files, the problem is that all the examples they provide both beta and gama are positive which makes the addition and interpretation process easy. my question is how to add and interpret when one is positive and the other is negative . As the effect of MV on realistic managers is - 0.0566 and on overconfident managers is + 0.0596 , it moves from negative to positive 0.003 (again the interaction is significant although at 10 level). how many times MV effect overconfident managers more than other managers ? I would really appreciate help in that Many thanks Nahla On 9 May 2013 19:36, David Crow <david.crow@cide.edu> wrote: > Dear Nahla- > > You're on the right track, but not quite right. I find that it's > helpful to think of the meaning of each coefficient. Let's boil your > model down to just the two variables (Market Value, MV, and > Overconfident Managers, OC), their interaction, and an intercept: > > y = B0 + B1*(MV) + B2*(OC) + B3*(MV*OC) + u > and yhat = B0 + B1*(MV) + B2*(OC) + B3*(MV*OC) > > Since OC is an indicator variable (overconfident = 1), when OC=0--that > is, for non-overconfident, or "realistic" managers", yhat is simply B0 > + B1*(MV) and the effect of market value is given by B1. However, > when OC=1--that is, for overconfident managers--yhat is > B0+B1*MV+B2*OC+B3*MV*OC. Since OC=1, this simplifies to > B0+B1*MV+B2+B3*MV and the effect of MV is given by B1+B2+B3 > > Your calculation (-0.0566241 + 0.0596146= 0.003) leaves out the term > B2, the coefficient for OC. So, the correct slopes are: > > OC=0: -0.0566241 > OC=1: -0.0566241 + -.1040174 + 0.0596146 = -.1010269. > > In this case, the effects of market value appear to attenuate the > effects of overconfidence. > > Hope this helps. > > Best, > David > > On Thu, May 9, 2013 at 8:20 AM, Nahla Betelmal <nahlaib@gmail.com> wrote: >> >> Dear Statalist, >> >> >> As you can see below, I have a interaction term between OC (dummy =1 >> for overconfidence) and MV (continuous variable for market value). The >> interaction term is positive and significant. I want to calculate the >> slope against MB for overconfident managers which should be the >> coefficient of MV plus the coefficient of OC*MV. >> I am confused how to get this figure because MV is negative and OC*MV >> is positive. So, Should it be -0.0566241 + 0.0596146= 0.003? if this >> is true how can I interpret how many times the effect of MV is larger >> for overconfident managers?? 0.003/0.0566. >> >> I am really confused and I highly appreciate your help please >> >> >> >> >> Linear regression Number of obs = 49 >> F( 10, 38) = 3.23 >> Prob > F = 0.0043 >> R-squared = 0.4385 >> Root MSE = .08529 >> >> ------------------------------------------------------------------------------ >> | Robust >> earnings managment| Coef. Std. Err. t P>|t| [95% >> Conf. Interval] >> -------------+---------------------------------------------------------------- >> var1 | .0081153 .0058432 1.39 0.173 -.0037137 .0199443 >> MV | -.0566241 .0353602 -1.60 0.118 -.128207 .0149588 >> var3| .1992782 .093338 2.14 0.039 .0103252 .3882312 >> var4 | -.0040891 .0109331 -0.37 0.710 -.0262219 .0180437 >> var5 | .0817256 .1169071 0.70 0.489 -.1549405 .3183917 >> var6 | .0291373 .026944 1.08 0.286 -.0254079 .0836825 >> var7 | -.0646094 .0320074 -2.02 0.051 -.129405 .0001863 >> var8 | -.0867868 .0311875 -2.78 0.008 -.1499227 -.0236509 >> OC| -.1040174 .0556577 -1.87 0.069 -.2166906 .0086558 >> OC*MV | .0596146 .0324333 1.84 0.074 -.0060433 .1252724 >> _cons | .1643745 .0994735 1.65 0.107 -.0369991 .365748 >> >> >> many Thanks >> >> Nahla Betelmal >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > > > > > -- > Web site for México, las Américas y el Mundo: > http://mexicoyelmundo.cide.edu/ > > ==================================== > David Crow > Profesor-Investigador/Assistant Professor > División de Estudios Internacionales > Carretera México-Toluca 3655 > Col. Lomas de Santa Fe 01210 México, D.F. > Tel.: 5727-9800, ext. 2152 > Fax: 5727-9872 > ==================================== > > Conmutador: 5727-98-00 Lada sin costo: 01 800 021 2433 (CIDE) |© > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: interpretation for negative and positive slope combination of interaction term***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: interpretation for negative and positive slope combination of interaction term***From:*tyaqub2003@yahoo.com

**References**:**st: interpretation for negative and positive slope combination of interaction term***From:*Nahla Betelmal <nahlaib@gmail.com>

**Re: st: interpretation for negative and positive slope combination of interaction term***From:*David Crow <david.crow@cide.edu>

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