A Tabu-based algorithm learns time-ordered causal graphs from time series by optimizing per-edge lags with a decomposable BIC score and explicit lag penalty.
Springer, Berlin, Heidelberg (2005)
5 Pith papers cite this work. Polarity classification is still indexing.
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A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
Dynamic directed spectral co-clustering on degree-corrected stochastic co-blockmodels embedded in VAR-type models uncovers latent community paths, with non-asymptotic misclassification bounds and applications to U.S. payrolls and global stock volatilities.
A new SMAR model is introduced and fit to 2021-2025 DJIA data, finding that volatility drives trading volume and that cross-asset spillovers explain over half of volume variation at longer horizons.
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
citing papers explorer
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Time series causal discovery with variable lags
A Tabu-based algorithm learns time-ordered causal graphs from time series by optimizing per-edge lags with a decomposable BIC score and explicit lag penalty.
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Tests for Independence of High-Dimensional Nonstationary Time Series
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
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Latent community paths in VAR-type models via dynamic directed spectral co-clustering
Dynamic directed spectral co-clustering on degree-corrected stochastic co-blockmodels embedded in VAR-type models uncovers latent community paths, with non-asymptotic misclassification bounds and applications to U.S. payrolls and global stock volatilities.
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A Structural Matrix Autoregressive Model for the Joint Dynamics of Volume, Volatility, and Returns
A new SMAR model is introduced and fit to 2021-2025 DJIA data, finding that volatility drives trading volume and that cross-asset spillovers explain over half of volume variation at longer horizons.
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Factor multivariate stochastic volatility models of high dimension
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.