Estimation for non-Gaussian locally stationary processes with empirical likelihood method

DOI10.1155/2012/704693zbMath1244.62120OpenAlexW2124729918WikidataQ58697839 ScholiaQ58697839MaRDI QIDQ444216

Hiroaki Ogata

Publication date: 14 August 2012

Published in: Advances in Decision Sciences (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/8217926009395913817cd4fcbc5f1b4d5bccc65a

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Asymptotic properties of parametric tests (62F05)

Related Items (1)

A review of empirical likelihood methods for time series

Cites Work

• Empirical likelihood methods with weakly dependent processes
• Empirical likelihood ratio confidence regions
• A central limit theorem for stationary processes and the parameter estimation of linear processes
• Empirical likelihood for linear models
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• Empirical likelihood and general estimating equations
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• Fitting time series models to nonstationary processes
• Parameter estimation and hypothesis testing in stationary vector time series
• On the Kullback-Leibler information divergence of locally stationary processes
• A frequency domain empirical likelihood for short- and long-range dependence
• Automatic methods for generating seismic intensity maps
• Empirical likelihood ratio confidence intervals for a single functional
• Empirical likelihood confidence regions in time series models

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