21 Jun
2005
21 Jun
'05
6:17 p.m.
I have been using the MI procedure in SAS, which converges slowly if at
all when the variables are nearly collinear. I am considering trying
Amelia -- will Amelia have an easier time with collinearity?
--
Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
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21 Jun
21 Jun
8:16 p.m.
I don't know the details of what SAS/MI does, but collinear variables
don't help predict much and will slow down convergence. You might as well
create an index from the collinear variables. Unless you're mainly
interested in the conditional effects of one given the other (in which
case your data won't have much info about it anyway), you won't lose much
in terms of bias, and the algorithm will go faster too.
Gary
---
Gary King
David Florence Professor of Government,
Director, Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
http://GKing.Harvard.Edu, King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
On Tue, 21 Jun 2005, Paul von Hippel wrote:
I have been using the MI procedure in SAS, which
converges slowly if at
all when the variables are nearly collinear. I am considering trying
Amelia -- will Amelia have an easier time with collinearity?
--
Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
-
Amelia mailing list served by Harvard-MIT Data Center
[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia
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Amelia mailing list served by Harvard-MIT Data Center
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9:29 p.m.
Thanks for your comments. It's complicated to explain why, but I actually
have an unusual situation where my analysis can make use of the collinear
variables. So my question is whether Amelia will handle collinearity
better than competing algorithms. King, Honaker, Joseph, and Scheve claim
that Amelia's algorithm has fewer convergence problems than leading
alternatives, and I'm wondering if those advantages hold up when some
variables are nearly collinear.
Gary King said:
I don't know the details of what SAS/MI does, but collinear variables
don't help predict much and will slow down convergence. You might as well
create an index from the collinear variables. Unless you're mainly
interested in the conditional effects of one given the other (in which
case your data won't have much info about it anyway), you won't lose much
in terms of bias, and the algorithm will go faster too.
Gary
---
Gary King
David Florence Professor of Government,
Director, Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
http://GKing.Harvard.Edu, King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
On Tue, 21 Jun 2005, Paul von Hippel wrote:
--
Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
300 Bricker Hall
190 N. Oval Mall
Columbus OH 43210
614 688-3768
Office hours TThF 3-5pm
I read email every weekday at 3.
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I have been using the MI procedure in SAS, which
converges slowly if at
all when the variables are nearly collinear. I am considering trying
Amelia -- will Amelia have an easier time with collinearity?
--
Paul von Hippel
Department of Sociology / Initiative in Population Research
Ohio State University
-
Amelia mailing list served by Harvard-MIT Data Center
[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia
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