Large sample properties of parameter least squares estimates for time‐varying arma models

DOI10.1111/j.1467-9892.2004.02003.xzbMath1062.62172OpenAlexW2074744299MaRDI QIDQ4677042

Antony Gautier, Christian Francq

Publication date: 20 May 2005

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2004.02003.x

zbMATH Keywords

consistencyasymptotic normalitylarge sample propertiestime-varying ARMA modelsparameter least squares estimates

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (11)

Autoregressive Order Identification for VAR Models with Non Constant Variance ⋮ Threshold Structures in Economic and Financial Time Series ⋮ Non-parametric estimation of time varying AR(1)-processes with local stationarity and periodicity ⋮ Adaptive estimation of vector autoregressive models with time-varying variance: application to testing linear causality in mean ⋮ Estimation of weak ARMA models with regime changes ⋮ A test for comparing two discrete stochastic dynamical systems under heteroskedasticity ⋮ Dynamic modeling of mean-reverting spreads for statistical arbitrage ⋮ A Bayesian analysis of moving average processes with time-varying parameters ⋮ Structure and estimation of a class of nonstationary yet nonexplosive GARCH models ⋮ Asymptotic Inefficiency of Mean-Correction on Parameter Estimation for a Periodic First-Order Autoregressive Model ⋮ Inference in Autoregression under Heteroskedasticity

Cites Work

• Random coefficient autoregressive models: an introduction
• Time series: theory and methods.
• Fitting time series models to nonstationary processes
• Estimation of time-varying ARMA models with Markovian changes in regime
• Large Sample Properties of Parameter Estimates for Periodic ARMA Models
• PARSIMONIOUS PERIODIC TIME SERIES MODELING
• Stochastic Limit Theory

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