its the standard deviation not of the bounds for one precinct, but the stan dev of width of the bounds across precincts. what helps improve 2nd stage estimation is both narrow average bounds and a large variance in the widths across precincts. Gary

I hope I'm not being too dense here, but that sounds like you get one result -- a number that represents the standard deviation of the widths across all of the precincts. But how do we use this one result to decide which precincts to toss out and which to include? It might help me if you explain, concretely, how this standard deviation is computed. Thanks, Greg

Hi Gary and list -- I have a few questions about EI-R and how to implement practically the AKHS prescriptions on using precinct-level EI estimates in second-stage regressions. for those of you who might know of it, I also refer below to a forthcoming article by HS in AJPS that revisits poor Burden and Kimball's 1998 APSR article. It develops a logical consistency test for potential EI-R applications, also makes strongly the case for the dominance of the extended EI model over EI-R. I would like to use extended EI directly, but am having some difficulties in accessing and interpreting the alpha parameters, also in fitting this into the 2x3 ei/ei2 framework. My problem attempts to exstimate and explain differences between list PR ballot voting and candidate-based single-member district ballot voting in the 1996 italian election. To summarize, here's my setup. I have a 2x3 table where I am interested in the total table values of the top row only. I run ei and ei2 to estimate beta^b_i and lambda^b_i, then combine them to get precinct-level parameters of interest. e.g. row 1 col 1 = beta^b_i*lambda^b_i. (I rely on the vector of _Esim simulated precinct-level parameters to do the multiplication, then take the mean for the combined parameter of interest.) for covariates I use zb = a scalar measure of policy position (the left-right midpoint between the two candidates in the single-member district). No covariate for zw since this is not a quantity of direct interest. The zb covariate is included in both ei and ei2. those combined params of interest are my precinct-level estimates of split voting. I then want to estimate the effect of policy positions of the precinct-candidates -- which varies by precinct -- on split voting. this involves using the estimates of the precinct-level params of interest as a dependent variable in EI-R. My reading of the lit I refer to above, and my e-mail exchanges with M Herron, all indicate that i should avoid EI-R and simply rely on the estimates of alpha from the extended EI model. I am willing to follow this advice, but have a few issues to think about and resolve. 1) According to AKHS, the bias from the HS/HS2-type problem can be detected based on the width of the bounds. My mean bound on lambda^b_i for example is wide at .82, putting it squarely into the "bias zone". This indicates I should avoid EI-R. 2) According to the test for logical consistency in HS-AJPS, I can detect a problem by regressing Zb_i on X_i and conducting a Wald test. My application actually passes this test, since the wald test fails to be significant for either X on zb from ei, or on (estimated) X from zb from ei2. This indicates possibly that EI-R would be okay. btw gary the test also promotes hair growth. 3) It seems from the interpretation of alpha^b in HS-AJPS that it can be directly interepreted as the effect of zb on shifts in the mean distribution from which beta^b_i is drawn. (For the superpopulation case which is what I am interested in.) That's okay with me. But where do I obtain this estimate? Is this the output column from eiread(db,"sum") labeled: Maximum likelihood results in scale of estimation (and se's) Zb0 Zb1 this would not seem to be alpha since the Zb0 seems to be a constant, and alpha is after entering zb as a deviation from its mean vector (thereby removing the constant). Do I have to estimate the alphas by hand based on p170 of your book? If so this is hardly something most people would be able to readily do. 4) AK states that "2nd stage analyses...have the advantage of being easier to understand, use, and evaluate" which is certainly true, all the more so for the 2x3 case as in my application. For me the best feature is that I have combined the beta^b_i and lambda^b_i estimates to get my overall split voting quantities of interest, and then can regress these directly. If I scrap the EI-R and use the extended model's alpha estiamtes directly, how can I combine the alphas from ei and ei2 to get measures of influence on my overall 2x3 table quantities of interest? (and still preserve standard errors?) Advice is much appreciated!! Ken ................................................................ Kenneth Benoit http://benoit.tcd.ie Department of Political Science mailto:kbenoit@tcd.ie Trinity College Tel: 353-1-608-2491 Dublin 2, Ireland...........................Fax: 353-1-677-0546