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Re: st: Reconcile Log Transformed with Untransformed Results


From   Erasmo Giambona <e.giambona@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Reconcile Log Transformed with Untransformed Results
Date   Thu, 25 Feb 2010 18:32:17 +0100

Thanks Tony. Actually, I take the log of 1+y. Yes, i tried glm with a
log link and that helps as well. The issue is that i found it
difficult to interpret the results in economic terms. All the details
are in the previous emails.
Erasmo

On Thu, Feb 25, 2010 at 6:24 PM, Lachenbruch, Peter
<Peter.Lachenbruch@oregonstate.edu> wrote:
> Since one of your y's is negative, -0.03, why should taking logs help? Would a glm with a log link help?
>
> Tony
>
> Peter A. Lachenbruch
> Department of Public Health
> Oregon State University
> Corvallis, OR 97330
> Phone: 541-737-3832
> FAX: 541-737-4001
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Erasmo Giambona
> Sent: Thursday, February 25, 2010 4:32 AM
> To: statalist@hsphsun2.harvard.edu
> 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,
>
> Erasmo
>
> On Tue, Feb 16, 2010 at 6:11 PM, Austin Nichols <austinnichols@gmail.com> wrote:
>> Erasmo Giambona <e.giambona@gmail.com>:
>> 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 <e.giambona@gmail.com> 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 <maartenbuis@yahoo.co.uk> 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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