Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach

DOI10.1111/sjos.12030zbMath1283.62189arXiv1211.1212OpenAlexW2120285300MaRDI QIDQ2868867

Natalie Neumeyer, Leonie Selk

Publication date: 19 December 2013

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.1212

zbMATH Keywords

time serieskernel estimationpartial sum processempirical distribution functionsconditional heteroscedasticitynonparametric AR-ARCH modelnonparametric CHARN model

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)

Related Items (9)

TESTING FOR A CHANGE OF THE INNOVATION DISTRIBUTION IN AN ARCH MODEL ⋮ Epidemic change tests for the mean of innovations of an AR(1) process ⋮ ON MULTIPLE STRUCTURAL BREAKS IN DISTRIBUTION: AN EMPIRICAL CHARACTERISTIC FUNCTION APPROACH ⋮ Change Point Detection with Multivariate Observations Based on Characteristic Functions ⋮ Specification testing in nonparametric AR‐ARCH models ⋮ A model specification test for the variance function in nonparametric regression ⋮ A note on non-parametric testing for Gaussian innovations in AR-ARCH models ⋮ 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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