Iterated logarithm law for sample generalized partial autocorrelations

DOI10.1016/S0167-7152(97)00130-2zbMath0904.60023MaRDI QIDQ1380590

D. Massart

Publication date: 18 January 1999

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

strong consistencyARMAergodic weakly stationary processgeneralized partial autocorrelation

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• An extension of Lai and Wei's law of the iterated logarithm with applications to time series analysis and regression
• Asymptotic properties of multivariate nonstationary processes with applications to autoregressions
• Limiting distributions of least squares estimates of unstable autoregressive processes
• A law of the iterated logarithm for double arrays of independent random variables with applications to regression and time series models
• ARMA model identification
• Asymptotic properties of general autoregressive models and strong consistency of least-squares estimates of their parameters
• Invariance principles for semi-stationary sequence of linear processes and applications to ARMA process
• Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models
• ON THE ASYMPTOTIC DISTRIBUTION OF THE GENERALIZED PARTIAL AUTOCORRELATION FUNCTION IN AUTOREGRESSIVE MOVING-AVERAGE PROCESSES
• On the Relationship Between the S Array and the Box-Jenkins Method of ARMA Model Identification

This page was built for publication: Iterated logarithm law for sample generalized partial autocorrelations