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Re: st: Poll of polls

From   Clive Nicholas <>
Subject   Re: st: Poll of polls
Date   Wed, 23 Jun 2010 00:42:04 +0100

Richard Ohrvall wrote:

> I am sorry if this is a bit too unspecific, but I am currently looking
> into what is usually called "poll of polls", i.e. techniques to take
> estimates from different opinion polls and estimating time series. I
> know, it is outside of established statistical theory, but I am
> playing around with political opinion polls to look at different
> methods to achieve time series. Some of the issues are a) how to
> handle "house effects", i.e. that different pollsters systematically
> diverge from others, b) how to smooth the data over time, e.g. some
> sort of moving average. So, my questions are 1) if any of you have
> seen anything done on this using Stata? 2) Do you have any
> ideas/suggestions about the best way to tackle this (e.g. if -lowess-
> is a path worth exploring)?


You should be clearer on what your response variable is. Is it voting
intention shares for political parties? Is it party-leader popularity
ratings? Are they measured monthly, quarterly or annually? Either way,
these are measured on the 0-100 scale and can pose their own problems
if their limits are reached. Analysed on their own on a house-by-house
basis would, in my view, call for normalizing the scales and
regressing them on your independent variables in a fractional logit or
probit model, viz:

glm y x, family(binomial) link(logit) robust


(a) if your intention is to model 'polling house' effects on opinion
shares across all polling houses and points of time simultaneously,
then this would call for a pooled TSCS (or CSTS) approach with the
polling houses included as fixed-effect dummies, so look at the family
of -xt- models and pick the one most suitable for your data;

(b) download Nick Cox and Kit Baum's -mvsumm- from SSC for creating
moving-average variables;


(c) -lowess- could be used to smooth time-series graphs, but also
check out Cox and Baum's -tsgraph- (SSC) or -help xtline-.

(Much) more details on your data, which reads very interestingly,
would help here.

Clive Nicholas

[Please DO NOT mail me personally here, but at
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"My colleagues in the social sciences talk a great deal about
methodology. I prefer to call it style." -- Freeman J. Dyson.
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