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Re: st: Interpretation of the OSL regression coefficient of a proportion dependent and interaction independent variable with dummy
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: Interpretation of the OSL regression coefficient of a proportion dependent and interaction independent variable with dummy
Date
Tue, 14 May 2013 17:43:06 +0100
This seems to boil down to
earnings mgt as a function of market/book ratio
-- I've no idea what either means, but let's hope that's not fatal --
with indicator (some say dummy) variables defining shifts and interactions.
That being so, surely a graph is what should make this clear, as those
regression lines can all be plotted in the same two-dimensional space.
In turn, that raises simple questions:
No intercept terms?
Does what is happening around (0, 0) make economic sense?
Are linear relationships adequate for the data?
When coefficients like 0.056 arise, my instinct is to rescale,
multiply small or divide big, according to whatever units people in
the field find comfortable.
Nick
[email protected]
On 14 May 2013 15:40, Nahla Betelmal <[email protected]> wrote:
> Dear Statlist,
>
> I have two regressions that I am not sure how to interpret the
> coefficient. In the first one the dependent variable is earnings
> management proxied by ( abnormal accruals scaled by total assets), the
> independent variable is an interaction between a dummy variable (type
> of managers OC) and valuation (proxied by a market to book ratio
> M/B) :
>
> A- earnings mgt= -0.0566MB + -0.10 Oc_dummy + 0.0569 MB*Oc-dummy
>
> Should I say OC mangers are likely to manage earnings by 5.69 cents
> per dollar increased in MB more than other managers. Or
>
> OC mangers are likely to manage earnings by 0.0569 percent for one
> percent increased in MB more than other managers
>
> The second regression has interaction between two dummy variables
> instead. MB is presented as dummy =1 if higher than industry average.
>
> B- earnings mgt= -0.056 MB + -0.004 Oc_dummy + 0.078 MB*Oc-dummy
>
> How can I interpret the coefficients in this case? Is it any different
>
> The interaction term in both regressions is significant.
>
> Thank you so much, I highly appreciate your kind help
>
> Nahla Betelmal
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