A CENTRAL LIMIT THEOREM FOR MIXING TRIANGULAR ARRAYS OF VARIABLES WHOSE DEPENDENCE IS ALLOWED TO GROW WITH THE SAMPLE SIZE

DOI10.1017/S0266466605050577zbMath1083.62076OpenAlexW2158463048MaRDI QIDQ3377447

Jean-Michel Zakoian, Christian Francq

Publication date: 22 March 2006

Published in: Econometric Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0266466605050577

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Non-Markovian processes: estimation (62M09)

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HAC estimation and strong linearity testing in weak ARMA models ⋮ Virtual historical simulation for estimating the conditional VaR of large portfolios ⋮ Convergence rates in the functional CLT for \(\alpha\)-mixing triangular arrays ⋮ Asymptotic distributions and subsampling in spectral analysis for almost periodically correlated time series ⋮ Higher-order properties of approximate estimators ⋮ Composite Quantile Periodogram for Spectral Analysis ⋮ Convergence of the empirical two-sample \(U\)-statistics with \(\beta\)-mixing data ⋮ Central limit theorem for quadratic errors of Nadaraya-Watson regression estimator under dependence ⋮ The central limit theorem for degenerate variableU-statistics under dependence ⋮ High dimensional generalized empirical likelihood for moment restrictions with dependent data ⋮ Of copulas, quantiles, ranks and spectra: an \(L_{1}\)-approach to spectral analysis

Cites Work

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