Re: st: Regressions with dependent continuous variable with bounded range

[Suryadipta Roy <sroy2138@gmail.com>]

Cam,
I do see that! I have an old version of Matlab that I will try to use.
Thanks again,
Suryadipta.

On Mon, Dec 19, 2011 at 12:47 PM, Cameron McIntosh <cnm100@hotmail.com> wrote:
> There is a free 7-day trial of Neusrel available right now... you do need Matlab to run it, however.Cam
>
> ----------------------------------------
>> Date: Mon, 19 Dec 2011 11:58:10 -0500
>> Subject: Re: st: Regressions with dependent continuous variable with bounded range
>> From: sroy2138@gmail.com
>> To: statalist@hsphsun2.harvard.edu
>>
>> Cam,
>> This is very interesting indeed! I had thought of SEM, but not this.
>>
>> Sincerely,
>> Suryadipta.
>>
>> On Mon, Dec 19, 2011 at 9:46 AM, Cameron McIntosh  wrote:
>> > An explorative approach to non-linearity might also be worth considering:
>> > Buckler, F., & Hennig-Thurau, T. (2008). Identifying Hidden Structures in Marketing’s Structural Models Through Universal Structure Modeling: An Explorative Bayesian Neural Network Complement to LISREL and PLS. Marketing -- Journal of Research and Management, 4(2), 47-66.http://www.neusrel.com/index.html
>> >
>> > Cam
>> >> Date: Mon, 19 Dec 2011 08:52:42 -0500
>> >> Subject: Re: st: Regressions with dependent continuous variable with bounded range
>> >> From: sroy2138@gmail.com
>> >> To: statalist@hsphsun2.harvard.edu
>> >>
>> >> Dear David,
>> >> Thank you very much for the useful suggestions! I completely
>> >> understand the points that have made, and will definitely explore
>> >> them. Actually, the incorporation of the quadratic x is driven by the
>> >> theoretical hypothesis, which has implications for the signs of x and
>> >> x-squared. A basic scatter diagram: twoway scatter y x, by(year) also
>> >> suggests non-linearity. I, of course, start with the linear form. We
>> >> can also probably compare between the models on the basis of LR tests,
>> >> or AIC/BIC criteria. Interestingly, a logit regression of the form
>> >> that Nick suggested gives me the (statistically significant) expected
>> >> signs of the coefficients. However, I would have to check the
>> >> robustness etc.
>> >>
>> >> Best regards,
>> >> Suryadipta.
>> >>
>> >> On Sun, Dec 18, 2011 at 2:11 PM, David Hoaglin  wrote:
>> >> > Dear All,
>> >> >
>> >> > Is it well-established that the effect is quadratic in x, as opposed
>> >> > to being nonlinear in x (the functional form might be quadratic or
>> >> > something else entirely)?  If the form is not necessarily quadratic, a
>> >> > good strategy would fit the linear term in x and then examine the
>> >> > pattern of nonlinearity by plotting the residuals against x.  A
>> >> > quadratic term can provide a reasonable approximation for some
>> >> > patterns of nonlinearity, but not for others.
>> >> >
>> >> > Also, centering x at a suitable value (often near the middle of its
>> >> > range) would be a good preliminary step.
>> >> >
>> >> > David Hoaglin
>> >> >
>> >> > On Sun, Dec 18, 2011 at 11:56 AM, Suryadipta Roy  wrote:
>> >> >> Dear Brendan and Nick,
>> >> >>
>> >> >> Thank you so much for the detailed suggestions! I will try to
>> >> >> implement these. Infact, I was just reading the paper by Papke and
>> >> >> Wooldridge (Journal of Econometrics, 2008) "Panel data methods for
>> >> >> fractional response variables with an application to test pass rates"
>> >> >> in order to understand the application better.
>> >> >>
>> >> >> Best regards,
>> >> >> Suryadipta.
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