On the rate of convergence to the normal law for LSE in multivariate continuous regression model with long-range dependence stationary errors

摘要

In this paper, we focus on a multivariate continuous regression model with long-memory stationary Gaussian errors. Some upper bounds on the rate of convergence in the Central Limit Theorem for normalized least square estimators (LSE) in these regression models are obtained. The used method is based on the asymptotic analysis of orthogonal expansion of non linear functionals of stationary Gaussian processes and on the Kolmogorov distance.