Asymptotic distributions of regression and autoregression coefficients with martingale difference disturbances

DOI10.1016/0047-259X(92)90024-AzbMath0755.62023MaRDI QIDQ1185832

Naoto Kunitomo, T. W. Anderson

Publication date: 28 June 1992

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

zbMATH Keywords

asymptotic normalitycentral limit theoremmartingale differencesleast squares estimatorLindeberg conditionsample covarianceregression coefficientsautoregression modelconditioned covariance matrices

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Central limit and other weak theorems (60F05)

Related Items (11)

Asymptotic robustness of tests of overidentification and predeterminedness ⋮ The consistency for the estimator of nonparametric regression model based on martingale difference errors ⋮ A Berry–Esseen theorem for sample quantiles under martingale difference sequences ⋮ Unnamed Item ⋮ Tests of overidentification and predeterminedness in simultaneous equation models ⋮ Asymptotic properties of LS estimators in the errors-in-variables model with MD errors ⋮ \(L_p\)-approximable sequences of vectors and limit distribution of quadratic forms of random variables ⋮ Asymptotic Properties of OLS Estimates in Autoregressions with Bounded or Slowly Growing Deterministic Trends ⋮ On the asymptotic optimality of the LIML estimator with possibly many instruments ⋮ Unnamed Item ⋮ Random decompositions of Eulerian statistics

Cites Work

• Asymptotic inference for nearly nonstationary AR(1) processes
• On Asymptotic Distributions of Estimates of Parameters of Stochastic Difference Equations
• Consistency and asymptotic efficiency of slope estimates in stochastic approximation schemes
• On Limit Theorems for Quadratic Functions of Discrete Time Series
• On the Statistical Treatment of Linear Stochastic Difference Equations
• Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems
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