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

[DE SOUZA Eric <eric.de_souza@coleurope.eu>]

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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Schaffer, Mark E
Sent: 21 February 2012 12:13
To: statalist@hsphsun2.harvard.edu
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: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Schaffer, 
> Mark E
> Sent: 21 February 2012 00:11
> To: statalist@hsphsun2.harvard.edu
> 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: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
> Schaffer,
> > Mark E
> > Sent: 20 February 2012 22:36
> > To: statalist@hsphsun2.harvard.edu
> > 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: owner-statalist@hsphsun2.harvard.edu
> > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf
> Of Miros Lav
> > > Sent: 20 February 2012 21:25
> > > To: statalist@hsphsun2.harvard.edu
> > > 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



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