Thanks Nick and SamL.
At first, I've tried not to include the Ln term and found that the model
suffer with the misspecification problem when I invoke linktest and ovtest
command and the single and quadratic term of age shows the same sign as
model 2 just now.
That come my idea to modified the variable into ln form. Off course my model
more likely to test the human capital theory from the perspectives of
non-technical workers in the industrial sectors.
Anyway thank for your comment and advice. I'll take it seriously.
Thanks guys
Rozilee
On 11/11/04 5:37 AM, "SamL" <[email protected]> wrote:
> Well, um, actually, there is an economic theoretic reason for the
> quadratic term in age, drawn from (among other sources) human capital
> theory--declining returns to _________________ (experience, prior
> training, fill in the blank with what you mean age to signify). So, I'm
> not sure I'd drop the linear term, as the theory does not imply only
> curving returns.
>
> There may be other economic theories that justify the quadratic and the
> linear term.
>
> Finally, statistically, removng the linear term implies no main effect.
> Does that make sense? It might help to graph the results. I think no
> linear term would be a major problem, but maybe not.
>
> HTH.
> Sam
>
> On Wed, 10 Nov 2004, Nick Cox wrote:
>
>> Are you really dealing with age or ln age?
>>
>> "Valid" or not depends on your criteria of
>> validity, which are not explicit. From what
>> I gather people like using quadratics in income
>> versus age because they often fit fairly well,
>> and there isn't a economic theory reason
>> for the functional form. So you could make
>> a case for dropping the linear term
>> if it doesn't to seem to help with the fit.
>>
>> On the other hand, there are several grounds
>> for being more circumspect:
>>
>> 1. Just because the linear term looks
>> insignificant does not mean that the
>> model with quadratic term alone is necessarly
>> better, all things considered.
>>
>> 2. The P-value is just one indicator. You
>> don't say anything about the change in R^2
>> or RMS error or (probably most important of
>> all) where there is clear structure
>> if you plot
>>
>> residuals from model with quadratic
>> term alone
>>
>> versus
>>
>> age.
>>
>> 3. Inferences are surely complicated by
>> the correlation between age and age^2.
>>
>> 4. There are good discussions of related
>> issues in McCullagh and Nelder's book
>> on generalised linear models and in
>> Nelder's paper in American Statistician
>> November 1998. Loosely, there are
>> grounds for treating polynomial terms
>> as yoked together like a team, although Nelder
>> puts it better than that.
>>
>> Nick
>> [email protected]
>>
>> Rozilee Asid
>>
>>> My wage model consists of several variables and model. One of my model
>>> consists of quadratic term of age, example
>>> Ln-wage = alpha0 + alpha1.ln_age + alpha2.ln_exp (model 1)
>>> Ln_wage = alpha0 + alpha1.ln_age + alpha2.ln_exp +
>>> alpha3.ln_age^2 (model 2)
>>>
>>> My main attention is to identified whether age play its
>>> significant role in
>>> the model. When I regress the model I found that alpha1 coefficient is
>>> negative and insignificant and alpha3 is positive and significant. My
>>> question is before I include the quadratic term of age
>>> variable (model 1),
>>> the alpha1 coefficient is positive and significant.
>>>
>>> Is it valid for me to report the finding from model 2
>>> equations, especially
>>> when alpha1 is negative in the model.
>>
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>>
> *
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