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From |
Boris Peko <borispeko@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: nonlinear N effect |

Date |
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 <maartenlbuis@gmail.com>: > 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 <maartenlbuis@gmail.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 >>> >>> >>> --------------------------------- >>> Maarten L. Buis >>> WZB >>> Reichpietschufer 50 >>> 10785 Berlin >>> Germany >>> >>> http://www.maartenbuis.nl >>> --------------------------------- >>> * >>> * 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 > WZB > Reichpietschufer 50 > 10785 Berlin > Germany > > http://www.maartenbuis.nl > --------------------------------- > * > * 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/

**References**:**st: nonlinear N effect***From:*Boris Peko <borispeko@gmail.com>

**Re: st: nonlinear N effect***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: nonlinear N effect***From:*Boris Peko <borispeko@gmail.com>

**Re: st: nonlinear N effect***From:*Maarten Buis <maartenlbuis@gmail.com>

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