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From |
"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Which inverter? (was: RE: RE: ivreg2 weak-id statistic and quadratic terms) |

Date |
Tue, 21 Feb 2012 19:21:13 -0000 |

Scott, I think you're right - it's the default tolerance. I tried setting the tolerance by hand for both svsolve() and qrsolve(), and if set small enough they both replicated -regress- and the scaled data. So I wasn't quite right. It's not the solver per se, but the default tolerance that it uses. Which raises somewhat different but still interesting questions: - Why is the default tolerance of svsolve() and qrsolve() in this example lax compared to luinv() and invsym()? - Is it generally the case that with scaling problems, svsolve() and/or qrsolve() will be more lax? I.e., was it a coincidence that Miroslav's example generated the same behaviour? Or might luinv() and/or invsym() sometimes be more lax? - Is there a simple way of checking for these things without adding much in the way of computational overhead? - Might it make sense to add to a future Mata wish-list an option for these solvers to return the tolerance to the user so that it can be checked? --Mark > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Scott Merryman > Sent: 21 February 2012 17:46 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: Which inverter? (was: RE: RE: ivreg2 weak-id > statistic and quadratic terms) > > Is it that the tolerance is very large due to the large value > of max(S)? Changing the tolerance does result in values that > are close to the scaled data: > > . *max(S) > > . mata: max(svdsv(XX)) > 1.31904e+15 > > . *value of tolerance > > . mata: max(svdsv(XX))*max(y)*epsilon(1) > 7.615041406 > > . mata: beta_sv2=svsolve(XX,Xy,.,-max(svdsv(XX))*max(y)*epsilon(1)) > > . mata: beta_sv2 > 1 > +----------------+ > 1 | .755934374 | > 2 | .0032936694 | > 3 | -1.97687e-06 | > 4 | .001060688 | > +----------------+ > > . mata: beta_sv2=svsolve(XX,Xy,.,-epsilon(1)) > > . mata: beta_sv2 > 1 > +----------------+ > 1 | .6979560211 | > 2 | -.0218987055 | > 3 | 1.97388e-06 | > 4 | 41.39947923 | > +----------------+ > > . *Compare to scaled data > > . mata: beta_sv1=svsolve(XX1,Xy1) > > . mata: beta_sv1 > 1 > +----------------+ > 1 | .6979557583 | > 2 | -21.89870042 | > 3 | 1.973884277 | > 4 | 41.39947954 | > +----------------+ > > > Scott > > > On Mon, Feb 20, 2012 at 6:11 PM, Schaffer, Mark E > <M.E.Schaffer@hw.ac.uk> wrote: > > Hi all. I have traced the problem to the choice of inverter. At > > least, it's definitely the problem in the auto dataset > example below, > > and I'll bet it's the source of Miroslav's problem as well. > > * > * 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/ > -- Heriot-Watt University is a Scottish charity registered under charity number SC000278. Heriot-Watt University is the Sunday Times Scottish University of the Year 2011-2012 * * 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**:**st: ivreg2 weak-id statistic and quadratic terms***From:*Miros Lav <ranjit.rosof.b189@googlemail.com>

**st: RE: ivreg2 weak-id statistic and quadratic terms***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

**st: Which inverter? (was: RE: RE: ivreg2 weak-id statistic and quadratic terms)***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

**Re: st: Which inverter? (was: RE: RE: ivreg2 weak-id statistic and quadratic terms)***From:*Scott Merryman <scott.merryman@gmail.com>

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