Estimation of the autoregressive order in the presence of measurement errors
Document Type
Journal article
Source Publication
Economics Bulletin
Publication Date
5-1-2006
Volume
3
Issue
12
First Page
1
Last Page
10
Keywords
Autoregressive Process, Measurement Error, Akaike Information Criterion, Bayesian Information Criterion
Abstract
Most of the existing autoregressive models presume that the observations are perfectly measured. In empirical studies, the variable of interest is unavoidably measured with various kinds of errors. Thus, misleading conclusions may be yielded due to the inconsistency of the parameter estimates caused by the measurement errors. Thus far, no theoretical result on the direction of bias of the lag order estimate is available in the literature. In this note, we will discuss the estimation an AR model in the presence of measurement errors. It is shown that the inclusion of measurement errors will drastically increase the complexity of the problem. We show that the lag lengths selected by the AIC and BIC are increasing with the sample size at a logarithmic rate.
Print ISSN
15452921
Publisher Statement
Copyright © 2006 Economics Bulletin.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Chong, T. T.-L., Liew, V., Zhang, Y., & Wong, C.-L. (2006). Estimation of the autoregressive order in the presence of measurement errors. Economics Bulletin, 3(12), 1-10.