Estimating RCARMA models with uncorrelated but non-independent error terms
share
In this paper we derive the asymptotic properties of the least squares estimator (LSE) of random coefficient autoregressive moving-average (RCARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. Relaxing the independence assumption considerably extends the range of application of the class of RCARMA models. Conditions are given for the consistency and asymptotic normality of the LSE. A particular attention is given to the estimation of the asymptotic variance matrix, which may be very different from that obtained in the standard framework. A set of Monte Carlo experiments is presented.
READ FULL TEXT