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st: Disappearing Statalist posts (was: RE: RE: RE: Which inverter? (was: RE: RE: ivreg2 weak-id statistic and quadratic terms))
From
"Schaffer, Mark E" <[email protected]>
To
<[email protected]>
Subject
st: Disappearing Statalist posts (was: RE: RE: RE: Which inverter? (was: RE: RE: ivreg2 weak-id statistic and quadratic terms))
Date
Tue, 21 Feb 2012 19:22:45 -0000
Eric,
I'm pretty sure that in my case it wasn't in the spam folder. Can't
speak for Miroslav, though.
--Mark
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of DE
> SOUZA Eric
> Sent: 21 February 2012 18:24
> To: [email protected]
> Subject: st: RE: RE: Which inverter? (was: RE: RE: ivreg2
> weak-id statistic and quadratic terms)
>
> Mark,
> Have you checked your spam folder? For some reason, several
> Statalist posts end up in my spam folder. I think that this
> is specific to the way is identified by our IT system here,
> but one never knows.
>
>
> Eric de Souza
> College of Europe
> Brugge (Bruges), Belgium
> http://www.coleurope.eu
>
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Schaffer, Mark E
> Sent: 21 February 2012 12:13
> To: [email protected]
> Subject: st: RE: Which inverter? (was: RE: RE: ivreg2 weak-id
> statistic and quadratic terms)
>
> Hi all. Just a quick update to say that Miroslav wrote to me
> directly and confirmed that the source of the problem was
> scaling in his case as well.
>
> --Mark
>
> NB: With apologies for the thread-changing ... Miroslav wrote
> to me directly because for some reason he didn't receive my
> Statalist post, and only spotted it on the Statalist archive.
> I've had a similar experience recently - something was
> posted to Statalist and showed up in the Statalist archive,
> but I didn't receive it as an email even though I was getting
> plenty of other Statalist posts at the time. Has anybody
> else had this problem?
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of
> Schaffer,
> > Mark E
> > Sent: 21 February 2012 00:11
> > To: [email protected]
> > Subject: st: Which inverter? (was: RE: RE: ivreg2 weak-id statistic
> > and quadratic terms)
> >
> > 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.
> >
> > In most places, -ivreg2- and -ranktest- use Mata's general-purpose
> > invsym() inverter. In a few places they use Mata's QR
> decomposition
> > qrsolve(). It is where the latter is used that things are going
> > wrong.
> > In the simple example I constructed with the auto data,
> > qrsolve() has problems but invsym() does not.
> >
> > This is an interesting question. What's the best choice of
> inverter
> > when faced with scaling problems?
> >
> > Below is some simple Mata code and output corresponding to a
> > regression with the toy auto data set when the variables create
> > scaling problems.
> > Stata's built-in -regress- is the benchmark.
> >
> > In a nutshell:
> >
> > invsym() reproduces the -regress- results.
> >
> > lusolve() reproduces the -regress- results.
> >
> > svsolve() runs into problems - without rescaling it goes
> badly wrong.
> >
> > qrsolve() runs into problems - without rescaling it goes
> badly wrong.
> >
> > I've had a look at the discussions in the manual, and I didn't spot
> > anything there that would explain this.
> >
> > Would someone who knows more about numerical computing than
> me care to
> > comment?
> >
> > --Mark
> >
> >
> > Stata code:
> >
> *********************************************************************
> > sysuse auto, clear
> > gen double weightsq=weight^2
> >
> > * Rescaled to reduce scaling problems
> > gen double weight1=weight/1000
> > gen double weight1sq=(weight/1000)^2
> >
> > * -regress- benchmark
> > qui reg mpg turn weight weightsq
> > mat list e(b)
> > qui reg mpg turn weight1 weight1sq
> > mat list e(b)
> >
> > putmata y=mpg X=(turn weight weightsq 1) X1=(turn weight1 weight1sq
> > 1), replace
> >
> > mata:
> >
> > XX=quadcross(X,X)
> > Xy=quadcross(X,y)
> > XX1=quadcross(X1,X1)
> > Xy1=quadcross(X1,y)
> >
> > "Comparing beta hat for (1) unscaled and (2) scaled data"
> >
> > beta_inv=invsym(XX)*Xy
> > beta_inv1=invsym(XX1)*Xy1
> > beta_inv, beta_inv1
> >
> > beta_lu=lusolve(XX,Xy)
> > beta_lu1=lusolve(XX1,Xy1)
> > beta_lu, beta_lu1
> >
> > beta_sv=svsolve(XX,Xy)
> > beta_sv1=svsolve(XX1,Xy1)
> > beta_sv, beta_sv1
> >
> > beta_qr=qrsolve(XX,Xy)
> > beta_qr1=qrsolve(XX1,Xy1)
> > beta_qr, beta_qr1
> >
> > end
> >
> *********************************************************************
> >
> >
> > Output (using Stata 11.2)
> >
> *********************************************************************
> > . sysuse auto, clear
> > (1978 Automobile Data)
> >
> > . gen double weightsq=weight^2
> >
> > .
> > . * Rescaled to reduce scaling problems . gen double
> > weight1=weight/1000
> >
> > . gen double weight1sq=(weight/1000)^2
> >
> > .
> > . * -regress- benchmark
> > . qui reg mpg turn weight weightsq
> >
> > . mat list e(b)
> >
> > e(b)[1,4]
> > turn weight weightsq _cons
> > y1 -.148733 -.01356202 1.345e-06 55.081765
> >
> > . qui reg mpg turn weight1 weight1sq
> >
> > . mat list e(b)
> >
> > e(b)[1,4]
> > turn weight1 weight1sq _cons
> > y1 -.148733 -13.562021 1.3448538 55.081765
> >
> > .
> > . putmata y=mpg X=(turn weight weightsq 1) X1=(turn weight1
> weight1sq
> > 1), replace
> > (1 vector, 2 matrices posted)
> >
> > .
> > . mata:
> > ------------------------------------------------- mata (type end to
> > exit) ----------------------------------
> > :
> > : XX=quadcross(X,X)
> >
> > : Xy=quadcross(X,y)
> >
> > : XX1=quadcross(X1,X1)
> >
> > : Xy1=quadcross(X1,y)
> >
> > :
> > : "Comparing beta hat for (1) unscaled and (2) scaled data"
> > Comparing beta hat for (1) unscaled and (2) scaled data
> >
> > :
> > : beta_inv=invsym(XX)*Xy
> >
> > : beta_inv1=invsym(XX1)*Xy1
> >
> > : beta_inv, beta_inv1
> > 1 2
> > +-------------------------------+
> > 1 | -.1487330027 -.1487330027 |
> > 2 | -.0135620214 -13.5620214 |
> > 3 | 1.34485e-06 1.344853823 |
> > 4 | 55.08176484 55.08176484 |
> > +-------------------------------+
> >
> > :
> > : beta_lu=lusolve(XX,Xy)
> >
> > : beta_lu1=lusolve(XX1,Xy1)
> >
> > : beta_lu, beta_lu1
> > 1 2
> > +-------------------------------+
> > 1 | -.1487330027 -.1487330027 |
> > 2 | -.0135620214 -13.5620214 |
> > 3 | 1.34485e-06 1.344853823 |
> > 4 | 55.08176484 55.08176484 |
> > +-------------------------------+
> >
> > :
> > : beta_sv=svsolve(XX,Xy)
> >
> > : beta_sv1=svsolve(XX1,Xy1)
> >
> > : beta_sv, beta_sv1
> > 1 2
> > +-------------------------------+
> > 1 | .6585549729 -.1487330027 |
> > 2 | .0057662425 -13.5620214 |
> > 3 | -2.30511e-06 1.344853823 |
> > 4 | .0096556129 55.08176484 |
> > +-------------------------------+
> >
> > :
> > : beta_qr=qrsolve(XX,Xy)
> >
> > : beta_qr1=qrsolve(XX1,Xy1)
> >
> > : beta_qr, beta_qr1
> > 1 2
> > +-------------------------------+
> > 1 | .6586963952 -.1487330027 |
> > 2 | .0057696338 -13.5620214 |
> > 3 | -2.30575e-06 1.344853823 |
> > 4 | 0 55.08176484 |
> > +-------------------------------+
> >
> > :
> > : end
> >
> *********************************************************************
> >
> > > -----Original Message-----
> > > From: [email protected]
> > > [mailto:[email protected]] On Behalf Of
> > Schaffer,
> > > Mark E
> > > Sent: 20 February 2012 22:36
> > > To: [email protected]
> > > Subject: st: RE: ivreg2 weak-id statistic and quadratic terms
> > >
> > > Hi Miroslav, hi all.
> > >
> > > I've checked this with the toy auto dataset. I can
> replicate this
> > > behaviour.
> > >
> > > Miroslav - either before or after rescaling your
> covariates, do the
> > > estimated coefficients vary hugely in scale?
> > >
> > > In my toy auto dataset example, I am pretty sure that it is
> > driven by
> > > scaling problems. For example, after
> > >
> > > sysuse auto, clear
> > > gen double weight2=weight^2
> > > ivreg2 price (mpg=turn) weight weight2
> > >
> > > gives a large weak ID stat of 11.5. But there are big scaling
> > > problems in the first-stage and main estimations, with
> > coeffs that are
> > > something like a factor of 10^8 different in magnitude.
> > >
> > > If I estimate and just partial out the constant,
> > >
> > > ivreg2 price (mpg=turn) weight weight2, partial(_cons)
> > >
> > > the ill-conditioning is less pronounced and I get a weak
> ID stat of
> > > 0.73.
> > >
> > > If I partial out all the exogenous covariates,
> > >
> > > ivreg2 price (mpg=turn) weight weight2, partial(weight weight2)
> > >
> > > the ill-conditioning is gone and I again get a weak ID
> stat of 0.73.
> > >
> > > I will investigate further and will report back to the list
> > if I find
> > > anything more. It may be that -ivreg2- could handle these
> > cases more
> > > robustly.
> > >
> > > --Mark (ivreg2 coauthor)
> > >
> > > > -----Original Message-----
> > > > From: [email protected]
> > > > [mailto:[email protected]] On Behalf
> > Of Miros Lav
> > > > Sent: 20 February 2012 21:25
> > > > To: [email protected]
> > > > Subject: st: ivreg2 weak-id statistic and quadratic terms
> > > >
> > > > Dear all,
> > > >
> > > > I am using ivreg2 to estimate a model where a control
> > > variable enters
> > > > with a quadratic term. A simplified version of the
> command is as
> > > > follows
> > > >
> > > > ivreg2 y (a=instrument) x x^2, r cluster(id).
> > > >
> > > > Estimating this model results in a very large
> > > Kleinbergen-Paap weak-id
> > > > F statistic.
> > > >
> > > > However, generating z=x/1000 and z^2=z*z and estimating
> the model
> > > >
> > > > ivreg2 y (a=instrument) z z^2, r cluster(id)
> > > >
> > > > results in a very low Kleinbergen-Paap weak-id F statistic.
> > > >
> > > > (The z-statistics and significance levels in the first
> and second
> > > > stage regressions are the same as in the previous model.)
> > > >
> > > > Does anyone have an idea why these two equivalent models
> > result in
> > > > very different Kleinbergen-Paap weak-id F statistic?
> > > >
> > > > Thanks for your help!
> > > >
> > > > Miroslav
> > > > *
> > > > * 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/
> > >
> >
> >
> > --
> > 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/
> >
>
>
> --
> 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/
>
> *
> * 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/