----------------------------------------
> Date: Mon, 9 May 2011 22:06:38 +1200
> From: alex.ols...@gmail.com
> To: pda...@gmail.com
> CC: r-help@r-project.org; da...@otter-rsch.com
> Subject: Re: [R] maximum likelihood convergence reproducing Anderson Blundell
> 1982 Econometrica R vs Stata
>
> Peter said
>
> "Ahem! You might get us interested in your problem, but not to the
> level that we are going to install Stata and Tsp and actually dig out
> and study the scientific paper you are talking about. Please cite the
> results and explain the differences."
>
> Apologies Peter, will do,
>
> The results which I can emulate in Stata but not (yet) in R are reported
> below.
did you actually cut/paste code anywhere and is your first coefficient -.19 or
-.019?
Presumably typos would be one possible problem.
> They come from Econometrica Vol. 50, No. 6 (Nov., 1982), pp. 1569
is this it, page 1559?
http://www.jstor.org/pss/1913396
generally it helps if we could at least see the equations to check your
code against typos ( note page number ?) in lnl that may fix part of the
mystery. Is full text available
on author's site, doesn't come up on citeseer AFAICT,
http://citeseerx.ist.psu.edu/search?q=blundell+1982&sort=ascdate
I guess one question would be " what is beta" in lnl supposed to be -
it isn't used anywhere but I will also mentioned I'm not that familiar
with the R code ( I'm trying to work through this to learn R and the
optimizers).
maybe some words would help, is sigma supposed to be 2x2 or 8x8 and what are
e1 and e2 supposed to be?
>
> TABLE II - model 18s
>
> coef std err
> p10 -0.19 0.078
> p11 0.220 0.019
> p12 -0.148 0.021
> p13 -0.072
> p20 0.893 0.072
> p21 -0.148
> p22 0.050 0.035
> p23 0.098
>
> The results which I produced in Stata are reported below.
> I spent the last hour rewriting the code to reproduce this - since I
> am now at home and not at work :(
> My results are "identical" to those published. The estimates are for
> a 3 equation symmetrical singular system.
> I have not bothered to report symmetrical results and have backed out
> an extra estimate using adding up constraints.
> I have also backed out all standard errors using the delta method.
>
> . ereturn display
> ------------------------------------------------------------------------------
> | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> a |
> a1 | -.0188115 .0767759 -0.25 0.806 -.1692895 .1316664
> a2 | .8926598 .0704068 12.68 0.000 .7546651 1.030655
> a3 | .1261517 .0590193 2.14 0.033 .010476 .2418275
> -------------+----------------------------------------------------------------
> g |
> g11 | .2199442 .0184075 11.95 0.000 .183866 .2560223
> g12 | -.1476856 .0211982 -6.97 0.000 -.1892334 -.1061378
> g13 | -.0722586 .0145154 -4.98 0.000 -.1007082 -.0438089
> g22 | .0496865 .0348052 1.43 0.153 -.0185305 .1179034
> g23 | .0979991 .0174397 5.62 0.000 .0638179 .1321803
> g33 | -.0257405 .0113869 -2.26 0.024 -.0480584 -.0034226
> ------------------------------------------------------------------------------
>
> In R I cannot get results like this - I think it is probably to do
> with my inability at using the optimisers well.
> Any pointers would be appreciated.
>
> Peter said "Are we maximizing over the same parameter space? You say
> that the estimates from the paper gives a log-likelihood of 54.04, but
> the exact solution clocked in at 76.74, which in my book is rather
> larger."
>
> I meant +54.04 > -76.74. It is quite common to get positive
> log-likelihoods in these system estimation.
>
> Kind regards,
>
> Alex
>
> On 9 May 2011 19:04, peter dalgaard wrote:
> >
> > On May 9, 2011, at 06:07 , Alex Olssen wrote:
> >
> >> Thank you all for your input.
> >>
> >> Unfortunately my problem is not yet resolved. Before I respond to
> >> individual comments I make a clarification:
> >>
> >> In Stata, using the same likelihood function as above, I can reproduce
> >> EXACTLY (to 3 decimal places or more, which is exactly considering I
> >> am using different software) the results from model 8 of the paper.
> >>
> >> I take this as an indication that I am using the same likelihood
> >> function as the authors, and that it does indeed work.
> >> The reason I am trying to estimate the model in R is because while
> >> Stata reproduces model 8 perfectly it has convergence
> >> difficulties for some of the other models.
> >>
> >> Peter Dalgaard,
> >>
> >> "Better starting values would help. In this case, almost too good
> >> values are available:
> >>
> >> start <- c(coef(lm(y1~x1+x2+x3)), coef(lm(y2~x1+x2+x3)))
> >>
> >> which appears to be the _exact_ solution."
> >>
> >> Thanks for the suggestion. Using these starting values produces the
> >> exact estimate that Dave Fournier emailed me.
> >> If these are the exact solution then why did the author publish
> >> different answers which are completely reproducible in
> >> Stata and Tsp?
> >
> >
> > Ahem! You might get us interested in your problem, but not to the level
> > that we are going to install Stata and Tsp and actually dig out and study
> > the scientific paper you are talking about. Please cite the results and
> > explain the differences.
> >
> > Are we maximizing over the same parameter space? You say that the estimates
> > from the paper gives a log-likelihood of 54.04, but the exact solution
> > clocked in at 76.74, which in my book is rather larger.
> >
> > Confused....
> >
> > -p
> >
> >
> >>
> >> Ravi,
> >>
> >> Thanks for introducing optimx to me, I am new to R. I completely
> >> agree that you can get higher log-likelihood values
> >> than what those obtained with optim and the starting values suggested
> >> by Peter. In fact, in Stata, when I reproduce
> >> the results of model 8 to more than 3 dp I get a log-likelihood of
> >> 54.039139.
> >>
> >> Furthermore if I estimate model 8 without symmetry imposed on the
> >> system I reproduce the Likelihood Ratio reported
> >> in the paper to 3 decimal places as well, suggesting that the
> >> log-likelihoods I am reporting differ from those in the paper
> >> only due to a constant.
> >>
> >> Thanks for your comments,
> >>
> >> I am still highly interested in knowing why the results of the
> >> optimisation in R are so different to those in Stata?
> >>
> >> I might try making my convergence requirements more stringent.
> >>
> >> Kind regards,
> >>
> >> Alex
> >
> > --
> > Peter Dalgaard
> > Center for Statistics, Copenhagen Business School
> > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> > Phone: (+45)38153501
> > Email: pd....@cbs.dk Priv: pda...@gmail.com
> >
> >
>
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