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| From | Suryadipta Roy <sroy2138@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Regressions with dependent continuous variable with bounded range |
| Date | Mon, 19 Dec 2011 11:58:10 -0500 |
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 <cnm100@hotmail.com> 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 <dchoaglin@gmail.com> 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 <sroy2138@gmail.com> 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. >> > * >> > * For searches and help try: >> > * http://www.stata.com/help.cgi?search >> > * http://www.stata.com/support/statalist/faq >> > * http://www.ats.ucla.edu/stat/stata/ >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/