Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: nonlinear N effect
Boris Peko <email@example.com>
Re: st: nonlinear N effect
Wed, 6 Mar 2013 14:43:32 +0100
My first goal is to find effect of managerial ownership on company
value. I predict it is non-linear and N-shaped.
2013/3/6 Maarten Buis <firstname.lastname@example.org>:
> I suspect that the null hypothesis you state is probably not the null
> hypothesis you want to test. Alternative null hypotheses (unfortunate
> choice of words, I know) would be: "the curve is N shaped" or "the
> curve is N shaped and the turning point happen at 5% and 25%".
> Once you have chosen your model and your null hypothis, you should
> rephrase the hypothesis in terms of the parameters of your model. Than
> the test is often a straightforward call to -test- or -testnl-.
> -- Maarten
> On Wed, Mar 6, 2013 at 2:09 PM, Boris Peko wrote:
>> My analysis is effect on managerial ownership on company's value and I
>> use panel models.
>> Ho is that man.own has unlinear effect (N-shape) on company's value.
>> Other authors have predetermined inflection (or turning) points of 5%
>> and 25% but I want to find out
>> techniques to find those points.
>> 2013/3/6 Maarten Buis <email@example.com>:
>>> On Wed, Mar 6, 2013 at 12:14 PM, Boris Peko wrote:
>>>> Hi! I have a non-linear effect but not U-shape or reverse U-shape.
>>>> Effect is N-shaped, dependent variable rises, then fell, then rises.
>>>> How can I test that in Stata?
>>>> More important, what method should I use if I do not want to use
>>>> predetermined inflection point?
>>> The test depends on the _exact_ null hypothesis and the method you
>>> used to estimate your model.
>>> One possibility would be to add a third degree polynomial (cubic
>>> curve), which could be an N-shaped curve. The extrema of this curve
>>> have a closed form solution, so you can quickly look if the turning
>>> point happen within the range of the data. -orthpoly- could be useful
>>> for reducing multicolinearity and improving the stability of the
>>> estimates. However, a cubic curve is often too restrictive and too
>>> sensitive to outliers for my taste, just as a second degree polynomial
>>> (quadratic curve) is often too restrictive and too sensitive to
>>> outliers for hypothesised U-shaped and reverse U-shaped curves.
>>> A better alternative would probably be -fracpoly-, but this also
>>> depends on all kinds of details of your data, the substantive
>>> background of your study, tribal habits within your
>>> (sub-(sub-))discipline, etc..
>>> Hope this helps,
>>> Maarten L. Buis
>>> Reichpietschufer 50
>>> 10785 Berlin
>>> * For searches and help try:
>>> * http://www.stata.com/help.cgi?search
>>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/
>> * http://www.ats.ucla.edu/stat/stata/
> Maarten L. Buis
> Reichpietschufer 50
> 10785 Berlin
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/
* For searches and help try: