Adaptive estimation of vector autoregressive models with time-varying variance: application to testing linear causality in mean

DOI10.1016/j.jspi.2012.04.005zbMath1335.62140arXiv1007.1193OpenAlexW1982581735MaRDI QIDQ452998

Hamdi Raïssi, Valentin Patilea

Publication date: 18 September 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1007.1193

zbMATH Keywords

kernel smoothingordinary least squaresadaptive least squaresBahadur relative efficiencyheteroscedastic errorslinear causality in meanVAR model

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05)

Related Items (7)

Autoregressive Order Identification for VAR Models with Non Constant Variance ⋮ Testing Second-Order Dynamics for Autoregressive Processes in Presence of Time-Varying Variance ⋮ ADAPTIVE LONG MEMORY TESTING UNDER HETEROSKEDASTICITY ⋮ Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models ⋮ Testing and Modelling for the Structural Change in Covariance Matrix Time Series With Multiplicative Form ⋮ Estimation of the variance function in structural break autoregressive models with non‐stationary and explosive segments ⋮ Level shift estimation in the presence of non-stationary volatility with an application to the unit root testing problem

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