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

DOI10.1016/S0096-3003(03)00145-0zbMath1035.62091MaRDI QIDQ1427872

Ahmed H. El-Bassiouny

Publication date: 14 March 2004

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

zbMATH Keywords

Hermite polynomialsKolmogorov distanceAsymptotic normalityLeast squares estimatorRate of convergenceLong-memory errorsMultivariate continuous regression models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05)

Uses Software

• longmemo

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic properties of LSE of regression coefficients on singular random fields observed on a sphere
• On the exactness of normal approximation of LSE of regression coefficient of long-memory random fields
• Exact parabolic asymptotics for singular \(n\)-D Burgers' random fields: Gaussian approximation
• Long memory processes and fractional integration in econometrics
• On estimation of regression coefficients of long memory random fields observed on the arrays
• Asymptotic properties of the LSE in a regression model with long-memory Gaussian and non-Gaussian stationary errors

This page was built for publication: On the rate of convergence to the normal law for LSE in multivariate continuous regression model with long-range dependence stationary errors.