Testing stationarity of functional time series

By Lajos Horv'ath, Piotr Kokoszka, Gregory Rice. 2014

230 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/46d670345d0a09e4f39758aee9e879342814bbab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Gregory Rice
2. Lajos Horv'ath
3. Alexander Aue
4. Anne van Delft
5. Holger Dette
6. Piotr Kokoszka
7. Han Lin Shang
8. Degui Li

In Finance

1. Gregory Rice
2. Lajos Horv'ath
3. Alexander Aue
4. Piotr Kokoszka
5. Holger Dette
6. Han Lin Shang
7. Kevin Kokot
8. Anne van Delft

Citations in the corpus

• Long-Range Dependent Curve Time Series (2020, methodology): “The transformation defined in (5.2) is applied to these age-specific fertility rates, and a stationarity test proposed by Horváth, Kokoszka, and Rice (2014) is used to examine whether or not the transformed functional time series is stationary.”
• Testing relevant hypotheses in functional time series via self‐normalization (2018, background): “(see section 4 in Horváth et al. (2013) for a similar approach), such that ∫ 1 0 μ 2 2.”
• Prediction of functional ARMA processes with an application to traffic data (2016, methodology): “Consequently, we restrict our investigation to working days (Monday-Friday), resulting in a functional time series X n for n = 1 , . . . , N = 119, for which the stationarity test suggested in [17] does not reject the stationarity assumption.”
• Extensions of some classical methods in change point analysis (2014, methodology): “An analog of the CUSUM process Hn(x) is introduced and studied in Horváth et al. (2014) in case of dependent observations.”
• Testing for stationarity of functional time series in the frequency domain (2017, methodology): “A different version of functional stationarity tests, based on time domain methodology involving cumulative sum statistics (Aue & Horváth, 2013), was given in Horváth et al. (2014).”
• Wasserstein autoregressive models for density time series (2020, background): “Research of this type has been done for linear functional time series, see, for example, Berkes et al. (2009), Horváth et al. (2014), Zhang and Shao (2015), but not for density times series.”
• Functional data analysis in the Banach space of continuous functions (2017, methodology): “[30] Lajos Horváth, Piotr Kokoszka, and Gregory Rice.”