CWLS and ML estimates in a heteroscedastic RCA(1) model

Janečková Hana and Prášková Zuzana

Statistics & Risk Modeling, 2004, vol. 22, issue 3, 245-259

Abstract: The paper concerns with parameter estimation in a heteroscedastic random coefficient autoregressive (RCA) model of the form Xt = btXt−1 + Yt. A conditionally weighted least squares (CWLS) estimator of β = Ebt is studied. Its strong consistency and asymptotic normality are proved. For this purpose theory of near-epoch dependent (NED) processes is used. Consistency results are also obtained in case that variances both of the random parameter and heterogeneous errors are unknown and have to be estimated. Some simulations are presented to support the theory.

Date: 2004
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DOI: 10.1524/stnd.22.3.245.57064

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