TESTING FOR A CHANGE IN CORRELATION AT AN UNKNOWN POINT IN TIME USING AN EXTENDED FUNCTIONAL DELTA METHOD

DOI10.1017/S0266466611000661zbMath1239.91187OpenAlexW2123562921MaRDI QIDQ2890704

Dominik Wied, Walter Kramer, Herold G. Dehling

Publication date: 11 June 2012

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

Full work available at URL: https://doi.org/10.1017/s0266466611000661

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Statistical methods; risk measures (91G70) Economic time series analysis (91B84)

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Cites Work

• Break detection in the covariance structure of multivariate time series models
• A new test for structural stability in the linear regression model
• GARCH (1,1) processes are near epoch dependent
• Range vs. maximum in the OLS-based version of the CUSUM test
• The sample autocorrelations of heavy-tailed processes with applications to ARCH
• Covariance changes detection in multivariate time series
• TESTING FOR DISTRIBUTIONAL CHANGE IN TIME SERIES
• Sample Cross-correlations for Moving Averages with Regularly Varying Tails
• GENERALIZED AUTOREGRESSIVE CONDITIONAL CORRELATION
• The Cusum Test with Ols Residuals
• Asymptotic Statistics
• A Conditional Kolmogorov Test
• Stochastic Limit Theory
• Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices
• METHODS OF STATISTICAL ANALYSIS APPROPRIATE FOR k SAMPLES OF TWO VARIABLES
• MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
• K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests