First-order random coefficient autoregressive (RCA(1)) model: Joint Whittle estimation and information


  • Mahendran Shitan Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia; Department of Mathematics, Faculty of Science, Universiti Putra Malaysia
  • A. Thavaneswaran Department of Statistics, University of Manitoba
  • Turaj Vazifedan Department of Mathematics, Faculty of Science, Universiti Putra Malaysia



Non-linear time series, RCA model, Whittle's estimation and information


Random coefficient autoregressive model, RCA(p), has been discussed widely as a suitable model for nonlinear time series. The conditional least squares and likelihood parameter estimation of RCA(p) model has also been discussed in [3]. The statistical inference of RCA(1) model has been presented in [4] while the conditional least square estimates for nonstationary processes is studied in [7]. The optimal estimation for nonlinear time series using estimating equations has been investigated in [6]. Recently there has been interest in joint prediction based on spectral density of popular nonlinear time series models such as RCA(p) models. Another way of estimating the parameters of the RCA(1) model is to do Whittle's estimation. In this paper the Whittle estimates of the parameters of an RCA(p) model are studied. It is shown that the Whittle information of the autoregressive parameter in an RCA(p) model is larger than the corresponding information in an autoregressive (AR) model.


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