Journal
STATISTICS
Volume 46, Issue 4, Pages 505-521Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02331888.2010.541557
Keywords
quasi-likelihood estimation; random coefficient autoregressions; integer-valued time series
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We examine the Gaussian quasi-maximum likelihood estimator (QMLE) for random coefficient autoregressions. Consistency and asymptotic normality are established for general random coefficients and general correlation structure between these coefficients and the noise. In particular, the obtained results apply even if the stationary solution has infinite absolute mean or infinite variance. Next an application to the integer-valued times series modelling is given which provides a novel alternative for traditional INAR-like models for these series.
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