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Asymptotic Inference for the Weak Stationary Double AR(1) Model

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DOI: 10.4236/ojs.2012.22016    3,667 Downloads   6,170 Views   Citations

ABSTRACT

An AR(1) model with ARCH(1) error structure is known as the first-order double autoregressive (DAR(1)) model. In this paper, a conditional likelihood based method is proposed to obtain inference for the two scalar parameters of interest of the DAR(1) model. Theoretically, the proposed method has rate of convergence O(n-3/2). Applying the proposed method to a real-life data set shows that the results obtained by the proposed method can be quite different from the results obtained by the existing methods. Results from Monte Carlo simulation studies illustrate the supreme accuracy of the proposed method even when the sample size is small.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

F. Chang, A. Wong and Y. Wu, "Asymptotic Inference for the Weak Stationary Double AR(1) Model," Open Journal of Statistics, Vol. 2 No. 2, 2012, pp. 141-152. doi: 10.4236/ojs.2012.22016.

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