Estimation of the Autoregressive Order in the Presence of Measurement Errors
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.
Volume (Year): 3 (2006)
Issue (Month): 12 ()
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- Chong, Terence Tai-Leung, 2001.
"Structural Change In Ar(1) Models,"
Cambridge University Press, vol. 17(01), pages 87-155, February.
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Departmental Working Papers
_088, Chinese University of Hong Kong, Department of Economics.
- 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.
- Seraph Xin Wang & Terence Tai-leung Chong & Haiqiang Chen, 2004.
"Generic Consistency of the Break-Point Estimators under Specification Errors in a Multiple-Break Model,"
Departmental Working Papers
_160, Chinese University of Hong Kong, Department of Economics.
- Jushan Bai & Haiqiang Chen & Terence Tai-Leung Chong & Seraph Xin Wang, 2008. "Generic consistency of the break-point estimators under specification errors in a multiple-break model," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 287-307, 07.
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