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December 10, 2025Statistical Methods & Applications4 citationsOpen Access

(Bi)spectral analysis of Markov switching bilinear time series

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MCMaddalena CavicchioliAGAhmed GhezalIZImane Zemmouri

Key Points

  • Spectral densities derived from Markov switching bilinear models provide crucial insights into time series behaviors.
  • Simulation study validates the asymptotic properties of density matrices, supporting their reliability in practical applications.
  • Higher-order moments and autocovariance functions are crucial for understanding the underlying dynamics of time series.
  • Analysis of frequency domain offers valuable interpretations for real-world time series data, enhancing analytical precision.

Abstract

Abstract We derive matrix expressions in closed form for the higher-order moments, the autocovariance function and the spectral and bispectral densities of Markov switching bilinear models and their powers. Under suitable assumptions, we prove that the sample estimators of the spectral and bispectral density matrices are consistent and asymptotically normally distributed. A simulation study confirms the validity of the asymptotic properties. These methods are also well suited for the analysis of time series in the frequency domain, as shown in some proposed real-world examples.

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Cite This Study

Cavicchioli et al. (2025) studied this question.

synapsesocial.com/papers/69401b372d562116f28f7fa8https://doi.org/10.1007/s10260-025-00826-9
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