Estimation of the Autoregressive Order in the Presence of Measurement Errors
Terence Tai Leung Chong (),
Chi Leung Wong and
Venus Liew ()
Economics Bulletin, 2006, vol. 3, issue 12, 1-10
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.
Keywords: Autoregressive; Process; Measurement; Error; Akaike; Information; Criterion; Bayesian; Information; Criterion (search for similar items in EconPapers)
JEL-codes: C2 C2 (search for similar items in EconPapers)
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