eprintid: 88
rev_number: 12
eprint_status: archive
userid: 25
dir: disk0/00/00/00/88
datestamp: 2011-02-22 14:39:24
lastmod: 2011-09-27 13:25:17
status_changed: 2011-02-22 14:39:24
type: monograph
metadata_visibility: show
contact_email: g.katz@imtlucca.it
item_issues_count: 0
creators_name: Katz, Gabriel
creators_name: Melton, James
creators_id: g.katz@imtlucca.it
creators_id: james.melton@imtlucca.it
title: Measurement Error and Dynamic Nonlinear Models:
(Over)Estimating the Effect of Habit
ispublished: unpub
subjects: HA
subjects: JF
divisions: EIC
full_text_status: none
monograph_type: working_paper
abstract: Estimates from non-linear models are known to be inconsistent when the dependent variable
is misclassified. Although methods have been developed to correct this inconsistency in static
non-linear models, no correction exists for dynamic non-linear models. This is a serious omission
from the literature. Since the lagged dependent variable is an explanatory variable in dynamic
models, any inconsistency that arises from misclassifcation of the dependent variable in a static
non-linear model will be magnifed when that model is made dynamic. Here, we demonstrate
this fact using the habitual voting literature and develop a parametric model to correct for this
inconsistency. We find that, on average, estimates of habitual voting are approximately twice as
large when using survey respondents' self-reports versus official records of their turnout decisions.
When we apply our corrected model to respondents' self-reports, however, the estimates of
habitual voting are significantly closer to those provided by the official records.
date: 2011
citation:   Katz, Gabriel and Melton, James  Measurement Error and Dynamic Nonlinear Models: (Over)Estimating the Effect of Habit.  Working Paper       (Unpublished)