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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2410.15187 |
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| _version_ | 1866916448567296000 |
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| author | Ghosh, Dhrubajyoti McElroy, Tucker Lahiri, Soumendra |
| author_facet | Ghosh, Dhrubajyoti McElroy, Tucker Lahiri, Soumendra |
| contents | Higher-order spectra (or polyspectra), defined as the Fourier Transform of a stationary process' autocumulants, are useful in the analysis of nonlinear and non Gaussian processes. Polyspectral means are weighted averages over Fourier frequencies of the polyspectra, and estimators can be constructed from analogous weighted averages of the higher-order periodogram (a statistic computed from the data sample's discrete Fourier Transform). We derive the asymptotic distribution of a class of polyspectral mean estimators, obtaining an exact expression for the limit distribution that depends on both the given weighting function as well as on higher-order spectra. Secondly, we use bispectral means to define a new test of the linear process hypothesis. Simulations document the finite sample properties of the asymptotic results. Two applications illustrate our results' utility: we test the linear process hypothesis for a Sunspot time series, and for the Gross Domestic Product we conduct a clustering exercise based on bispectral means with different weight functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyspectral Mean Estimation of General Nonlinear Processes Ghosh, Dhrubajyoti McElroy, Tucker Lahiri, Soumendra Statistics Theory Higher-order spectra (or polyspectra), defined as the Fourier Transform of a stationary process' autocumulants, are useful in the analysis of nonlinear and non Gaussian processes. Polyspectral means are weighted averages over Fourier frequencies of the polyspectra, and estimators can be constructed from analogous weighted averages of the higher-order periodogram (a statistic computed from the data sample's discrete Fourier Transform). We derive the asymptotic distribution of a class of polyspectral mean estimators, obtaining an exact expression for the limit distribution that depends on both the given weighting function as well as on higher-order spectra. Secondly, we use bispectral means to define a new test of the linear process hypothesis. Simulations document the finite sample properties of the asymptotic results. Two applications illustrate our results' utility: we test the linear process hypothesis for a Sunspot time series, and for the Gross Domestic Product we conduct a clustering exercise based on bispectral means with different weight functions. |
| title | Polyspectral Mean Estimation of General Nonlinear Processes |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2410.15187 |