Nonparametric statistics for testing of linearity and serial independence

DOI10.1080/10485259608832673zbMath0880.62049OpenAlexW2016548833MaRDI QIDQ4345897

Vidar Hjellvik, Dag Tjøstheim

Publication date: 5 February 1998

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

Full work available at URL: https://doi.org/10.1080/10485259608832673

zbMATH Keywords

kernel methodconditional meanlinearity testtest of serial independence

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (16)

Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks ⋮ An updated review of goodness-of-fit tests for regression models ⋮ A loss function approach to model specification testing and its relative efficiency ⋮ Optimal Detection of Exponential Component in Autoregressive Models ⋮ A note on testing symmetry of the error distribution in linear regression models ⋮ Nonparametric bootstrap tests for neglected nonlinearity in time series regression models^∗ ⋮ Determination of linear components in additive models ⋮ A simultaneous test for conditional mean and conditional variance functions in time series models with martingale difference innovations ⋮ A generalization of some classical time series tools ⋮ Nonparametric Estimation and Testing in Panels of Intercorrelated Time Series ⋮ Using local linear kernel smoothers to test the lack of fit of nonlinear regression models ⋮ Nonparametric model checks for time series ⋮ Locally asymptotically optimal tests for AR\((p)\) against diagonal bilinear dependence ⋮ Linearity testing using local polynomial approximation ⋮ Towards a nonparametric test of linearity for times series ⋮ Some higher-order theory for a consistent non-parametric model specification test

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