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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Polynomial Fitting and RD Design |

Date |
Thu, 1 Sep 2011 07:43:44 -0400 |

Maarten-- Note that the standard for this design is local linear regression with a triangle (AKA edge) kernel, as implemented in -rd- on SSC. But the poster asked about replication, not an optimal design. On Thu, Sep 1, 2011 at 5:24 AM, Maarten Buis <maartenlbuis@gmail.com> wrote: > On Thu, Sep 1, 2011 at 10:31 AM, Nick Cox wrote: >> Sure, but that still leaves the non-numeric issues. I guess the main >> issue is just reproducing behaviour with smooth curves, but what >> arguments justify any kind of quartic here? > > No disagreement with you on that point. Actually I think that such > high degree polynomial is rather dangerous for this purpose as these > curves tend to move rather wildly away from the data at the extreme > ends of the curve, and in these models the break is such an extreme > end. As a consequence the break dummy may just capture this misfit to > the data rather than a real break. Patrick may want to consider a > fractional polynomial model instead. Below is an example on how to > estimate both models, the graph shows that the quartic curve does show > that wild behavior at the break, and the fractional polynomial model > shows that that is due to overfitting the curve as in this case two > linear curves will do just fine. > > *--------------- begin example ----------------- > sysuse uslifeexp, clear > drop if year == 1918 // Spanish flu pandemic > gen cyear = year - 1950 // center at break > > // 4th degree polynomial > orthpoly cyear , gen(oyear*) degree(4) > gen D = cyear > 0 if year < . > forvalues i = 1/4 { > gen oyear`i'l = (1-D)*oyear`i' > } > forvalues i = 1/4 { > gen oyear`i'r = D*oyear`i' > } > > // fit model > reg le oyear?? D > > // predict outcome > predict pol > > // fractional polynomial > gen cyearl = (1-D)*cyear > gen cyearr = D*cyear > > // fit model > mfp, df(8) : reg le cyearl cyearr D > > // predict outcome > predict mfp > > // Graph the models > twoway line le pol mfp year, /// > xline(1950) /// > lstyle(solid solid solid) /// > lcolor(black red blue) /// > legend(order( 1 "data" /// > 2 "quartic" /// > 3 "fractional" /// > "polynomial" )) > *---------------- end example ------------------ > (For more on examples I sent to the Statalist see: > http://www.maartenbuis.nl/example_faq ) > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > * > * 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/

**References**:**Re: st: Polynomial Fitting and RD Design***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Polynomial Fitting and RD Design***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Polynomial Fitting and RD Design***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Polynomial Fitting and RD Design***From:*Maarten Buis <maartenlbuis@gmail.com>

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