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Re: st: interpretation for negative and positive slope combination of interaction term
From
Nahla Betelmal <[email protected]>
To
[email protected]
Subject
Re: st: interpretation for negative and positive slope combination of interaction term
Date
Thu, 9 May 2013 21:06:49 +0100
Sorry tyaqub2003, I cant see the content of your email. Can you resend
it again please.
thank you
nahla
On 9 May 2013 20:51, <[email protected]> wrote:
> Sent from my BlackBerry® smartphone provided by Airtel Nigeria.
>
> -----Original Message-----
> From: Nahla Betelmal <[email protected]>
> Sender: [email protected]
> Date: Thu, 9 May 2013 20:51:21
> To: <[email protected]>
> Reply-To: [email protected]
> Subject: Re: st: interpretation for negative and positive slope combination of
> interaction term
>
> 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 <[email protected]> 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 <[email protected]> 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
>> ====================================
>>
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>>
>> *
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>
> *
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