Parameter Stability and Semiparametric Inference in Time Varying Auto-Regressive Conditional Heteroscedasticity Models

DOI10.1111/RSSB.12221OpenAlexW2556036483MaRDI QIDQ4603790

Lionel Truquet

Publication date: 19 February 2018

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.02984

zbMATH Keywords

kernel smoothingsemiparametric inferencelocally stationary time seriesauto-regressive conditional heteroscedasticity processes

Mathematics Subject Classification ID

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

Related Items (12)

Nonparametric regression for locally stationary random fields under stochastic sampling design ⋮ Nonparametric regression for locally stationary functional time series ⋮ Efficient semiparametric estimation in time-varying regression models ⋮ Contrast estimation of time-varying infinite memory processes ⋮ Time-varying multivariate causal processes ⋮ Locally Stationary Multiplicative Volatility Modeling ⋮ Estimation, Inference, and Empirical Analysis for Time-Varying VAR Models ⋮ A perturbation analysis of Markov chains models with time-varying parameters ⋮ Two‐Step Estimation for Time Varying Arch Models ⋮ Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model ⋮ On Eagleson's theorem in the non‐stationary setup ⋮ Local stationarity and time-inhomogeneous Markov chains

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