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RE: st: Reconcile Log Transformed with Untransformed Results

From   "Lachenbruch, Peter" <>
To   "''" <>
Subject   RE: st: Reconcile Log Transformed with Untransformed Results
Date   Thu, 25 Feb 2010 09:24:03 -0800

Since one of your y's is negative, -0.03, why should taking logs help? Would a glm with a log link help?


Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001

-----Original Message-----
From: [] On Behalf Of Erasmo Giambona
Sent: Thursday, February 25, 2010 4:32 AM
Subject: Re: st: Reconcile Log Transformed with Untransformed Results

Thanks Austin. I have been traveling so it has been difficult to look
into this issue. To answer your question. I am using a two-step
procedure that is used sometime in monetary policy research. My y is a
coefficient estimated from a panel regression using firm level data.
This is the first step. y ranges from -0.03 to +0.07 (with mean=0.023,
median=0.024, st dev=0.028, skew=-.37, kurt= 2.52). I have 16 y's, one
per year. In the secon step i regress y on x, where x is an annual
interest rate spread ranging from -.95% to 1.15% (with mean=3.96e-07,
median=.0004551, st dev=.6426913, skew=.1102487, kurt= 2.15). The
scatter of y on x clearly shows that y increase with x, but there is
one obs (out of the 16) with a very low x and a very high y. I am
taking the logs to try to reduce the effetc of this obs. Thought this
is more parimonious relative to the alternative of dropping hte obs
and winsorizing seems unfeasible with 16 obs.

Any additional thoughts would be appreciated,


On Tue, Feb 16, 2010 at 6:11 PM, Austin Nichols <> wrote:
> Erasmo Giambona <>:
> As I already pointed out, I doubt your estimates correspond to any
> well-defined percentage point change.  Perhaps you can give us a
> better sense of the distributions of the untransformed y and x (and
> what they measure and in what units), and what the scatterplot of y
> against x looks like.  You may also prefer to state your effects in
> terms of standard deviations rather than the interquartile range.
> On Tue, Feb 16, 2010 at 9:39 AM, Erasmo Giambona <> wrote:
>> Thanks Maarten. In this example, OLS and GLM give very similar
>> econimic effects. In fact, 74 cents for the OLS is really 9.52%
>> relative to the mean wage of 7.77. This 9.52% is very much in line
>> with the 9.7% found with GLM. In my case, the coeff. on X for the OLS
>> is 0.0064. Relative to the mean for the LHS variable of 0.02. This is
>> an economic effect of about 28%. With the GLS, using exactly your
>> code, X gets a coefficient of 2.025 or a 102.5% increase in Y. Or
>> perhaps, I am misinterpreting this coefficient.
>> Thanks,
>> Erasmo
>> On Mon, Feb 15, 2010 at 9:22 AM, Maarten buis <> wrote:
>>> --- On Sun, 14/2/10, Erasmo Giambona wrote:
>>>> I ran the regressions with both RHS and LHS untransformed
>>>> using both OLS and GLM with link(log). With the OLS the
>>>> coeff on X is 0.006 while with the GLM the coefficient is
>>>> 0.700. I find a bit hard to intepret the GLM coefficient.
>>> Consider the example below:
>>> *--------------- begin example -----------------
>>> sysuse nlsw88, clear
>>> gen byte baseline =1
>>> reg wage grade
>>> glm wage grade baseline,  ///
>>>    link(log) eform nocons
>>> *--------------- end example --------------------
>>> The -regress- results are interpreted as follows:
>>> People without education can expect a wage of
>>> -1.96 dollars an hour (substantively we know that
>>> people hardly ever pay for the privelege to work,
>>> so this is a sign of bad model fit), and they get
>>> 74 cents an hour more of every additional year of
>>> education.
>>> The -glm- results are interpreted as follows:
>>> People without education can expect a wage of
>>> 2.25 dollars an hour, and for every additional
>>> year of education they can expect an increase
>>> of 9.7%.
>>> Hope this helps,
>>> Maarten
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