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Large Sample Analysis Of Autoregressive Moving‐Average Models With Errors In Variables

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  • Kamal C. Chanda

Abstract

. We consider estimation of parameters of an unobservable ARMA(p, q) process {Ut; t= 1,2,…} based on a set of n observables, X1, …, Xn, where Xt=Ut, +εt, 1 ≤t≤n, it being assumed that {εt} is independent of {Ut}. We examine the asymptotic properties of these ARMA estimators under a set of weak regularity conditions on {εt}.

Suggested Citation

  • Kamal C. Chanda, 1995. "Large Sample Analysis Of Autoregressive Moving‐Average Models With Errors In Variables," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(1), pages 1-15, January.
    • Handle: RePEc:bla:jtsera:v:16:y:1995:i:1:p:1-15
    DOI: 10.1111/j.1467-9892.1995.tb00220.x
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    References listed on IDEAS

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    1. B. D. O. Anderson & M. Deistler, 1984. "Identifiability In Dynamic Errors‐In‐Variables Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(1), pages 1-13, January.
    2. Robinson, P. M., 1986. "On the errors-in-variables problem for time series," Journal of Multivariate Analysis, Elsevier, vol. 19(2), pages 240-250, August.
    3. Moran, P. A. P., 1971. "Estimating structural and functional relationships," Journal of Multivariate Analysis, Elsevier, vol. 1(2), pages 232-255, June.
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    Cited by:

    1. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.

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