Weak convergence of non-stationary multivariate marked processes with applications to martingale testing

DOI10.1016/j.jmva.2007.03.004zbMath1116.62084OpenAlexW2146219297MaRDI QIDQ996976

Juan Carlos Escanciano

Publication date: 19 July 2007

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2007.03.004

zbMATH Keywords

weak convergencenon-stationary time seriesmartingale hypothesismarked empirical processes

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Monte Carlo methods (65C05) Non-Markovian processes: hypothesis testing (62M07)

Related Items (11)

Joint parametric specification checking of conditional mean and volatility in time series models with martingale difference innovations ⋮ Testing for serial independence of panel errors ⋮ An updated review of goodness-of-fit tests for regression models ⋮ Weak convergence of marked empirical processes for focused inference on \(\mathrm{AR}(p)\) vs \(\mathrm{AR}(p+1)\) stationary time series ⋮ Marked empirical processes for non-stationary time series ⋮ Weighted resampling of martingale difference arrays with applications ⋮ Weak convergence of marked empirical processes in a Hilbert space and its applications ⋮ Model checks for nonlinear cointegrating regression ⋮ Goodness-of-fit test for a nonlinear time series ⋮ Argmax-stable marked empirical processes ⋮ A Nonparametric Distribution-Free Test for Serial Independence of Errors

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