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Re: st: Using natural logs on RHS of maximum likelihood models


From   SAM MCCAW <sam2stata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Using natural logs on RHS of maximum likelihood models
Date   Thu, 28 Mar 2013 17:43:43 -0400

Dear David,

Thanks so much for your helpful reply.

I am using a logistic regression model where the dependent variable is
a (0,1) dichotomous variable which stands for whether a firm
undertakes innovation or not. The predictor variables include a number
of firm level and industry level characteristics, including firm R&D
share, industry presence of foreign firms, number of competitors in
the industry and others.

When I am working with a non-dichotomous variable on the LHS I prefer
using natural logs as I am interested in estimating elasticities.
Since I am not very experienced with logistic models I was wondering
if there was a rule of thumb in using transformations.

Please let me know if I can provide any further information. I would
appreciate your comments.

Thanks a lot!

SAM



On Tue, Mar 26, 2013 at 10:18 PM, David Hoaglin <dchoaglin@gmail.com> wrote:
> Dear Sam,
>
> Before anyone can make a constructive suggestion, you need to share
> more information on the details of your model.  Maximum likelihood is
> a method of estimating the parameters in a model.  It applies to a
> very wide range of models, some of which have a dichotomous (0, 1)
> outcome variable.  Is your model a logistic regression (logit) model,
> a probit model, or another type entirely?  Please be specific.
>
> Your question about the predictor variables does not have any single,
> simple answer.  The aim is usually to express each predictor variable
> in a form that appropriately captures its relation to the outcome
> variable (after adjusting for the contributions of the other predictor
> variables).  Generically, we could write something like
>
> g(y) = b0 + b1*f1(x1) + b2*f2(x2) + (more predictors)
>
> The functions g, f1, f2, etc. may differ as needed, and common choices
> include "leave it alone," take the logarithm, take the square root,
> and "square it."  Part of your challenge in analyzing data is to make
> appropriate choices of such functions.  For some classes of models,
> people have developed a variety of diagnostic tools that help this
> process.  Once you have explained more about your model and the
> context of your data, I or someone else reading this list may be able
> to recommend a book that discusses this and other steps in the
> model-building process and shows how they work on actual sets of data.
>
> I hope these comments are helpful.
>
> Regards,
>
> David Hoaglin
>
> On Tue, Mar 26, 2013 at 5:19 PM, SAM MCCAW <sam2stata@gmail.com> wrote:
>> Hello All,
>>
>> I am running a maximum likelihood model with a (0,1) categorical
>> dependent variable.
>>
>> On the right hand side is better to use natural logs of non
>> categorical variables or leave them as is as real numbers?
>>
>> Thanks a bunch.
>>
>> SAM
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