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MEASUREMENT ERRORS IN DYNAMIC MODELS

Published online by Cambridge University Press:  07 August 2013

Ivana Komunjer
Affiliation:
University of California, San Diego
Serena Ng*
Affiliation:
Columbia University
*
*Address correspondence to Serena Ng, Columbia University, 420 W. 118 St. MC 3308, New York, NY 10027; e-mail: serena.ng@columbia.edu.

Abstract

Static models that are not identifiable in the presence of white noise measurement errors are known to be potentially identifiable when the model has dynamics. However, few results are available for the plausible case of serially correlated measurement errors. This paper provides order and rank conditions for “limited information” identification of parameters in dynamic models with measurement errors where some aspects of the probability model are not fully specified or utilized. The key is to consider a model for the contaminated data that has richer dynamics than the model for the correctly observed data. Simply counting the total number of unknown parameters in the true model relative to the estimable model will not yield an informative order condition for identification. Implications for single-equation, vector autoregressive, and panel data models are studied.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2013 

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MEASUREMENT ERRORS IN DYNAMIC MODELS
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