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

[Nahla Betelmal <nahlaib@gmail.com>]

Nothing. the email is still empty. it only says: Sent from my
BlackBerry® smartphone provided by Airtel Nigeria.
and I am desperate for help plz

Thanks again

nahla
On 9 May 2013 21:09,  <tyaqub2003@yahoo.com> wrote:
> Sent from my BlackBerry® smartphone provided by Airtel Nigeria.
>
> -----Original Message-----
> From: David Crow <david.crow@cide.edu>
> Sender: owner-statalist@hsphsun2.harvard.edu
> Date: Thu, 9 May 2013 14:46:20
> To: <statalist@hsphsun2.harvard.edu>
> Reply-To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: interpretation for negative and positive slope combination of
>  interaction term
>
> Dear Nahla-
>
> You're on the right track, but not quite right.  I find that it's good
> 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 =3D B0 + B1*(MV) + B2*(OC) + B3*(MV*OC) + u
>
> and
>
> yhat =3D B0 + B1*(MV) + B2*(OC) + B3*(MV*OC)
>
> Since OC is an indicator variable (overconfident =3D 1), when
> OC=3D0--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=3D1--that is, for overconfident managers--yhat is
> B0+B1*MV+B2*OC+B3*MV*OC.  Since OC=3D1, 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=3D 0.003) leaves out the term
> B2, the coefficient for OC.  So, the correct slopes are:
>
> OC=3D0:  -0.0566241
> OC=3D1:  -0.0566241 + -.1040174 + 0.0596146 =3D -.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
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>
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