Hi David, I might be wrong, but I think the fact the theta parameter governs the equation zeta_i ~ (1/theta) * {something} means theta can't be zero (because 1/0 is undefined) and so has to be reparameterised accordingly for unconstrained optimisation. (Also, I think by definition, as it's something one is searching for the ML estimator for, theta isn't an ancillary parameter.) http://gking.harvard.edu/zelig/docs/Model20.html I think it's this reparameterised version (which is valid during the whole real number line, rather than just the positive part) which is part of the variance-covariance matrix. One has an approximation of the standard errors on this from the square root of the last element of the diagonal (from top left to bottom right) of this matrix. One can de-reparameterise this to get standard errors on the theta. (slightly confusingly, the var-cov matrix emerges from differentiating twice a vector comprising the betas then the reparameterised theta: this 'stack' is conventionally called 'theta', though in this case one of the parameters one is trying to find the ML estimator for is also called 'theta'...) "how do I get back a correct variance-cov matrix?" I think the short answer is that, as long as the reparameterisation of theta is valid for the whole real number line, then this is the 'correct' var-covar matrix, as one needs to feed this into the MVnormal later to get estimates (which take into account fundamental uncertainty) of the unconstrained versions of the parameters. These estimates can then be converted into the appropriate mus {e^(X*beta), so constrained across the positive part of the real number line} and thetas (which, as I said, I think can't be 0, so have to be constrained accordingly) when simulating for the quantities of interest. (OK, that's not really a short answer!) Clear? (probably not... any clearer explanations/corrections welcome) Best, Jon -----Original Message----- From: gov2001-l-bounces at lists.fas.harvard.edu [mailto:gov2001-l-bounces at lists.fas.harvard.edu] On Behalf Of landau at fas.harvard.edu Sent: 10 April 2007 05:08 To: gov2001-l at lists.fas.harvard.edu Subject: [gov2001-l] reparameterization for 2c Hi all, It seems like you need to reparameterize the ancillary parameter on part c in the Negative Binomial section but not the coefficient parameters (far from sure about this though). This works fine for the ancillary parameter itself, which you can then easily transform back. But how do I get back a correct variance-cov matrix? Holger had some code on this for section 4 where he simulated a parameter, transformed it, and then got CIs. But things seem more complex where you have a full variance-cov matrix, and particularly where you aren't reparameterizing everything. Thanks, David _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l -- No virus found in this incoming message. Checked by AVG Free Edition. Version: 7.5.446 / Virus Database: 269.0.0/754 - Release Date: 09/04/2007 22:59 -- No virus found in this outgoing message. Checked by AVG Free Edition. Version: 7.5.446 / Virus Database: 269.0.0/754 - Release Date: 09/04/2007 22:59

Hi, I have a quick question. I was tabulating income value in pset7. it supposed to have 5 (1~5) category according to the "readme" file. but in the dataset, category is from 0~5. What should I do with the groups that answers income ==0? tomoko

Hi, I am slightly confused by question b. We are told to first estimate propensity scores and than use matchit to obtain our matched dataset. Does this mean that we should obtain a propensity score for each observation using a logit (or some other CDF) and than use this vector of scores in matchit to generate our matched dataset? If this is the case, should we not be using the "distance" argument in matchit() to try out different models for calculating propensity scores? Thanks, Miya -- K. Miya Woolfalk Graduate Student Harvard University Government and Social Policy Quoting Holger Lutz Kern <hlk23 at cornell.edu>:

Thanks for the response. I have a follow up question. Does this mean that we should have matchit(formula = treatment ~ propensity.scores ... ) as our syntax? Or is there another argument that I am overlooking in the help file for matchit. Thanks! Miya -- K. Miya Woolfalk Graduate Student Harvard University Government and Social Policy Quoting Holger Lutz Kern <hlk23 at cornell.edu>: