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Re: st: indicator variable and interaction term different signs but both significant


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu
Subject   Re: st: indicator variable and interaction term different signs but both significant
Date   Sat, 06 Apr 2013 19:01:24 -0400

The coefficient for OC-D is the predicted difference between an overconfident manager and a regular manager when MV = 0 and the values of other variables are the same for both. Since such a person may not even exist (e.g. can MV even equal zero?) this may not be particularly interesting. See

http://www3.nd.edu/~rwilliam/xsoc63993/l53.pdf

for a discussion of how to interpret main effects when a model has interaction terms and ways to make results more interpretable. In your case you might want to center MV (i.e. subtract the mean from each case) and possibly other independent variables. Or, try drawing some graphs. Or, just don't worry about it too much. Once you add an interaction term, the interpretation of the effect of OC_D is very different.

Finally, as a sidelight, I would recommend using factor variable notation if you are using Stata 12, because this will let you use the margins and marginsplot commands. The above handout shows some examples.

At 04:45 PM 4/6/2013, Nahla Betelmal wrote:
Dear Statalist,

I am having difficulty interpreting the results from OSL regression. I
am trying to see whether Overconfident managers manipulate earnings in
a certain context.

The dependent variable earnings_Mgt is continuous  The problem is that
the indicator variable for overconfidence (OC_D) is negative and
significant, while the interaction between the indicator variable and
market_value variable (OC_MV) is positive and significant. What does
that mean?
Does it mean that overconfident managers manipulate earnings less than
others (rational managers), but when the market value is high they
manipulate earnings more than rational managers do?

Your help is highly appreciated,

many thanks

Nahla Betelmal


Linear regression               Number of obs   = 56
F( 8, 47)       = 3.60
Prob > F        = 0.0025
R-squared       = 0.3719
Root MSE        = .08355
Robust
earnings Mgt Coef. Std. Err. t P>t [95% Conf. Interval] size .0058268 .0092169 0.63 0.530 -.0127153 .0243689 leverage .0924198 .0724032 1.28 0.208 -.0532367 .2380763 MV .0046896 .0032752 1.43 0.159 -.0018993 .0112784 litigation .0310148 .0267527 1.16 0.252 -.0228048 .0848344 private_D -.0638102 .023056 -2.77 0.008 -.110193 -.0174275 same_D -.08197 .0273465 -3.00 0.004 -.136984 -.026956 OC_D -.0730767 .0288269 -2.54 0.015 -.131069 -.0150844
OC_MV          .0105348 .0049493        2.13    0.039   .000578 .0204916
_cons .0381444 .0615391 0.62 0.538 -.0856564 .1619452
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Richard Williams, Notre Dame Dept of Sociology
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