relation: http://eprints.imtlucca.it/88/
title: Measurement Error and Dynamic Nonlinear Models:  (Over)Estimating the Effect of Habit
creator: Katz, Gabriel
creator: Melton, James
subject: HA Statistics
subject: JF Political institutions (General)
description: 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
type: Working Paper
type: NonPeerReviewed
identifier:   Katz, Gabriel and Melton, James  Measurement Error and Dynamic Nonlinear Models: (Over)Estimating the Effect of Habit.  Working Paper       (Unpublished)