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Stationarity and Goodness‐of‐Fit Tests for Locally Stationary Time Series

Jean‐Marc Bardet

Journal of Time Series Analysis2026https://doi.org/10.1111/jtsa.70053preprint
AJG 3ABDC A
Weight
0.37

What the paper says

This paper considers the trajectory of a time series with time‐varying coefficients and proposes to test the adequacy of these parameters at a finite and fixed number of instants of the trajectory. For this purpose, a Wald test is constructed from point estimates of the parameters obtained by minimization of a kernel contrast. This can take the form of a localized near‐maximum likelihood estimator for ARMA or GARCH processes, or a localized least squares estimator for a GLARCH process, but many other time‐varying time series such as AR, ARCH, ARMA‐GARCH, APARCH,…, could be considered. Above all, this allows the introduction of a new stationarity test for these processes, whose very good numerical performance has been demonstrated by numerical experiments.

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https://doi.org/https://doi.org/10.1111/jtsa.70053

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@article{jean‐marc2026,
  title        = {{Stationarity and Goodness‐of‐Fit Tests for Locally Stationary Time Series}},
  author       = {Jean‐Marc Bardet},
  journal      = {Journal of Time Series Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/jtsa.70053},
}

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Stationarity and Goodness‐of‐Fit Tests for Locally Stationary Time Series

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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