Semiparametric Estimation by Model Selection for Locally Stationary Processes

DOI10.1111/j.1467-9868.2006.00564.xzbMath1110.62119OpenAlexW2146239295MaRDI QIDQ3442935

Sébastien Van Bellegem, Rainer Dahlhaus

Publication date: 24 May 2007

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: http://publications.ut-capitole.fr/2894/1/semiparametric.pdf

zbMATH Keywords

model selectionsieve estimatorWhittle likelihoodlocally stationary processempirical spectral processtime-varying autoregressive process

Mathematics Subject Classification ID

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

Related Items

Local inference for locally stationary time series based on the empirical spectral measure, Empirical spectral processes for locally stationary time series, Statistical inference in partially time-varying coefficient models, Order selection for heteroscedastic autoregression: a study on concentration, A Stratified Penalized Kernel Method for Semiparametric Variable Labeling and Estimation of Multi-Output Time-Varying Coefficient Models for Nonstationary Time Series, Time-varying additive model with autoregressive errors for locally stationary time series, Semiparametric model building for regression models with time-varying parameters, On parameter estimation for locally stationary long-memory processes, Frequency Domain Tests of Semiparametric Hypotheses for Locally Stationary Processes, Testing for white noise against locally stationary alternatives

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