Elements of nonlinear time series analysis and forecasting

DOI10.1007/978-3-319-43252-6zbMath1376.62001OpenAlexW2597958895MaRDI QIDQ516108

Jan G. De Gooijer

Publication date: 21 March 2017

Published in: Springer Series in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-43252-6

zbMATH Keywords

predictionestimationMarkov chainsforecastingnonlinear time seriesnonparametric testparametric univariate nonlinear models

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Prediction theory (aspects of stochastic processes) (60G25)

Related Items (8)

A multi-step kernel–based regression estimator that adapts to error distributions of unknown form ⋮ On operator fractional Lévy motion: integral representations and time-reversibility ⋮ Bootstrap confidence intervals for conditional density function in Markov processes ⋮ Adaptive test for periodicity in restrictive EXPAR(p) models ⋮ Self‐Weighted Lad‐Based Inference for Heavy‐Tailed Continuous Threshold Autoregressive Models ⋮ Revisiting the Canadian Lynx Time Series Analysis Through TARMA Models ⋮ Comparative analysis of robust and classical methods for estimating the parameters of a threshold autoregression equation ⋮ The integrated copula spectrum

Uses Software

• Matlab
• CAViaR

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