Strong consistency of least squares estimates in linear regression models driven by semimartingales

DOI10.1016/0047-259X(87)90179-5zbMath0627.62089MaRDI QIDQ1092578

Marek Musiela, Alain Le Breton

Publication date: 1987

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

semimartingalestrong consistency of least squares estimatesMultiple linear regression modelsnon random regressors in continuous time

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Strong limit theorems (60F15)

Related Items (5)

Weighted least squares estimates in linear regression models for processes with uncorrelated increments ⋮ Parameter estimation in optional semimartingale regression models ⋮ Linear sufficiency and linear admissibility in a continuous time Gauss-Markov model. ⋮ Order of convergence of regression parameter estimates in models with infinite variance ⋮ On sequential estimation of parameters in semimartingale regression models with continuous time parameter.

Cites Work

• Calcul stochastique et problèmes de martingales
• Strong consistency of least squares estimates in multiple regression II
• Convergence systems and strong consistency of least squares estimates in regression models
• Strong consistency of least squares estimates in normal linear regression
• A strong law of large numbers for vector Gaussian martingales and a statistical application in linear regression
• Weak and strong consistency of the least squares estimators in regression models
• Strong consistency of least-squares estimates in regression models
• Strong consistency of least squares estimates in multiple regression
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