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

[Cameron McIntosh <cnm100@hotmail.com>]

There is a free 7-day trial of Neusrel available right now... you do need Matlab to run it, however.Cam

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> 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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