Katz, Gabriel and Melton, James Measurement Error and Dynamic Nonlinear Models: (Over)Estimating the Effect of Habit. Working Paper (Unpublished)Full text not available from this repository.
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.
|Item Type:||Working Paper (Working Paper)|
|Subjects:||H Social Sciences > HA Statistics
J Political Science > JF Political institutions (General)
|Research Area:||Economics and Institutional Change|
|Depositing User:||Users 25 not found.|
|Date Deposited:||22 Feb 2011 14:39|
|Last Modified:||27 Sep 2011 13:25|
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