Time series: theory and methods
Time series: theory and methods
Methods for recursive robust estimation of AR parameters
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
HAC estimation and strong linearity testing in weak ARMA models
Journal of Multivariate Analysis
Computational Statistics & Data Analysis
Efficient Monte Carlo computation of Fisher information matrix using prior information
Computational Statistics & Data Analysis
Maximum likelihood estimation in nonlinear mixed effects models
Computational Statistics & Data Analysis
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Numerous time series admit weak autoregressive-moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. Analytic expressions are given for these information matrices, and consistent estimators, at any point of the parameter space, are proposed. The theoretical results are illustrated by means of Monte Carlo experiments and by analyzing the dynamics of daily returns and squared daily returns of financial series.