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Measurement Models for Time Series Analysis: Estimating Dynamic Linear Errors-in-Variables Models

Published online by Cambridge University Press:  04 January 2017

Gregory E. McAvoy
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Abstract

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This article uses state space modeling and Kalman filtering to estimate a dynamic linear errors-in-variables model with random measurement error in both the dependent and independent variables. I begin with a general description of the dynamic errors-in-variables model, translate it into state space form, and show how it can be estimated via the Kalman filter. I report the results of a simulation in which the amount of random measurement error is varied, to demonstrate the importance of estimating measurement error models and the superiority that Kalman filtering has over regression. I use the model in a substantive example to examine the effects of public opinion regarding nuclear power on the enforcement decisions of the Nuclear Regulatory Commission. I then estimate a dynamic linear errors-in-variables model using multiple indicators for the latent variables and compare simulations of this model to the single indicator model. Finally, I provide substantive examples which examine the effect of people's economic expectations on their approval of the president and their approval of government more generally.

Information

Type
Research Article
Information
Political Analysis , Volume 7 , 1998 , pp. 165 - 186
Copyright
Copyright © Society for Political Methodology 

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