POLMETH Archives

Political Methodology Society

POLMETH@LISTSERV.WUSTL.EDU

Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Subject:
From:
David Barker <[log in to unmask]>
Reply To:
Political Methodology Society <[log in to unmask]>
Date:
Fri, 21 Jul 2006 14:05:24 -0400
Content-Type:
text/plain
Parts/Attachments:
text/plain (36 lines)
Does anyone know a simple command or function that can be downloaded for
STATA that will calculate the conditional standard errors for multiplicative
variables within the OGLM framework? I have used both the lincom command and
the inteff command for this in the past, but I have never been working
within OGLM before. Now I have data in which the dv is both ordinal and
skewed, and for which estimating the heteroskedastic structure has
theoretical relevance. Thus, I am running a heteroskedastic ordinal cloglog
regression -- again, using the oglm command with cloglog specified as the
link function, adn the variables biasing the variance structure specified
with a "het" option, following the main command. Using this framework, the
inteff and lincom commands don't work. Does anyone know what I should do,
short of hand calculation?



David Barker
Associate Professor and Director of Graduate Studies
Department of Political Science
University of Pittsburgh
Pittsburgh, PA 15260
412-648-7275
412-648-7277 (fax)
[log in to unmask]

**********************************************************
             Political Methodology E-Mail List
        Editor: Karen Long Jusko <[log in to unmask]>
**********************************************************
        Send messages to [log in to unmask]
  To join the list, cancel your subscription, or modify
           your subscription settings visit:

          http://polmeth.wustl.edu/polmeth.php

********************************************************** 

ATOM RSS1 RSS2