# AW: st: AW: Economic Significance and Logged Independent Variables

 From "Martin Weiss" To Subject AW: st: AW: Economic Significance and Logged Independent Variables Date Wed, 8 Jul 2009 16:10:33 +0200

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posted, I could not possibly suggest that it is not correct. What is it,
though, that you read into the result of this calculation? With this
information in hand, I _may_ be able to give you a hint whether the strategy
is valid...

HTH
Martin

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Erasmo Giambona
Gesendet: Mittwoch, 8. Juli 2009 15:44
An: statalist@hsphsun2.harvard.edu
Betreff: Re: st: AW: Economic Significance and Logged Independent Variables

Thanks Martin. Sorry, I think something is not completely clear to me.
What I am doing to get the economic effect in the case of the logged
covariate is to multiply the raw coefficient (on the logged covariate)
by the standard deviation of the logged covariate. Are you suggesting
that this is not correct?

Thanks,

Erasmo

On Wed, Jul 8, 2009 at 3:19 PM, Martin Weiss<martin.weiss1@gmx.de> wrote:
>
> <>
>
> You put a covariate into logs, right? So the interpretation should be that
a
> one percent increase (not standard deviation) in this covariate causes an
> absolute increase in the dependent to the tune of the respective
> coefficient. The huge difference can be traced back to this different
> interpretation of the output, as the example shows:
>
>
> ***
> sysuse auto, clear
> reg we pr len he
> loc level=_b[pr]
> gen lnpr=log(pr)
> reg we lnpr len he
>
> di in red "Coeff in levels: " /*
>  */ `level' ", in logs: `=_b[lnpr]'"
> ***
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Erasmo
Giambona
> Gesendet: Mittwoch, 8. Juli 2009 15:09
> An: statalist
> Betreff: st: Economic Significance and Logged Independent Variables
>
> Dear Statalist,
>
> I have a panel dataset for a sample of publicly listed firms.
>
> I am fitting the following model using OLS: Debt/Total Assetsi = a +
> b*ln_Total_Assets + control variables + firm dummies + year dummies +
> ei. - where i is a subscript for firm i.
>
> The dependent variable is total Debt divided by Total Assets (both
> expressed in millions), which is a ratio ranging between 0 and 1;
> ln_Total_Assets is the natural logarithm of total assets.
>
> The output of the above regression shows that ln_Total_Asset is
> statistically significant at the 1% level. This variable has also a
> huge economic effect. In fact, a 1 standard deviation increase in
> ln_Total Assets causes Debt/Total Assetsi to increase by 0.15 (while
> its average is 0.202).
>
> Then, I run Debt/Total Assetsi = a + b*Total_Assets + control
> variables + firm dummies + year dummies + ei. This model differs from
> the above one only because I am not logging Total_Assets. In this
> case, I find that Total Assets is still highly statistically
> significant at the 1% level. However, its economic effect is
> negligible. In fact, a 1 standard deviation increase in Total Assets
> causes Debt/Total Assetsi to increase by 0.0002 (while its average is
> 0.202).
>
> I can see that logging a variable can make a difference on its
> economic effect. However, changing the economic effect from 0.15 to
> 0.0002 seems really a big difference. Can somebody provide some hints
> on why this might be happening? Is that an indicatio that there might
> be something special about the structure of my data?
>
> I would really appreciate any suggestions.
>
> Thanks,
>
> Erasmo
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