A note on non-parametric testing for Gaussian innovations in AR-ARCH models

DOI10.1111/jtsa.12018zbMath1273.62218arXiv1211.1204OpenAlexW2132324993MaRDI QIDQ2852597

Leonie Selk, Natalie Neumeyer

Publication date: 9 October 2013

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

Full work available at URL: https://arxiv.org/abs/1211.1204

zbMATH Keywords

kernel estimationempirical distribution functionautoregressionconditional heteroscedasticitynonparametric conditional heteroscedastic autoregressive nonlinear model

Mathematics Subject Classification ID

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

Related Items (5)

Estimating the Error Distribution in a Single-Index Model ⋮ Oracally efficient estimation of autoregressive error distribution with simultaneous confidence band ⋮ A model specification test for the variance function in nonparametric regression ⋮ Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach ⋮ Goodness-of-fit testing of error distribution in nonparametric ARCH(1) models

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