This paper deals with the estimation of unconditional and conditional Granger-causality spectrum in the frequency domain. We describe two Python routines that parallel the existing R routines in computing these two quantities via package grangers. We present a simulation study showing that under zero-causality processes Python routines tend to perform slightly better than R, while under low-causality processes R routines perform quite better, because Python is less sensitive than R to small causality parameters. This difference can be attributed to the intrinsic VAR order selection procedure of the two packages.

Matteo Farne, Meng Yang (2024). Comparing How Python and R Estimate Granger-Causality in the Frequency Domain. Cham : Springer [10.1007/978-3-031-53717-2_20].

Comparing How Python and R Estimate Granger-Causality in the Frequency Domain

Matteo Farne;
2024

Abstract

This paper deals with the estimation of unconditional and conditional Granger-causality spectrum in the frequency domain. We describe two Python routines that parallel the existing R routines in computing these two quantities via package grangers. We present a simulation study showing that under zero-causality processes Python routines tend to perform slightly better than R, while under low-causality processes R routines perform quite better, because Python is less sensitive than R to small causality parameters. This difference can be attributed to the intrinsic VAR order selection procedure of the two packages.
2024
Computing, Internet of Things and Data Analytics. ICCIDA 2023. Studies in Computational Intelligence
213
222
Matteo Farne, Meng Yang (2024). Comparing How Python and R Estimate Granger-Causality in the Frequency Domain. Cham : Springer [10.1007/978-3-031-53717-2_20].
Matteo Farne; Meng Yang
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/960469
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