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Estimation of the Autoregressive Order in the Presence of Measurement Errors

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  • Terence Tai-Leung Chong

    ()
    (The Chinese University of Hong Kong)

  • Chi-Leung Wong

    ()
    (University of British Columbia)

  • Venus Liew

    ()
    (Universiti Putra Malaysi)

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.

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File URL: http://www.accessecon.com/pubs/EB/2006/Volume3/EB-06C20003A.pdf
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Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 3 (2006)
Issue (Month): 12 ()
Pages: 1-10

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Handle: RePEc:ebl:ecbull:eb-06c20003

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Keywords: Autoregressive Process Measurement Error Akaike Information Criterion Bayesian Information Criterion;

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  1. Venus Khim-Sen Liew & Terence Tai-leung Chong, 2005. "Autoregressive Lag Length Selection Criteria in the Presence of ARCH Errors," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-5.
  2. Chong, Terence Tai-Leung, 2000. "Estimating the differencing parameter via the partial autocorrelation function," Journal of Econometrics, Elsevier, vol. 97(2), pages 365-381, August.
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