In this work we present a nonparametric test to detect nonlinearity in time series. The test is based on permutation methods and essentially it is a distributional comparison between two sub-populations. We evaluate the significance nominal level and the power of tests by simulation considering differen t linear and nonlinear models. As benchmarking we use some well known nonlinearity tests.
Nonparametric strategies applied to time series analysis
PIZZI, Claudio;PARPINEL, Francesca
2006-01-01
Abstract
In this work we present a nonparametric test to detect nonlinearity in time series. The test is based on permutation methods and essentially it is a distributional comparison between two sub-populations. We evaluate the significance nominal level and the power of tests by simulation considering differen t linear and nonlinear models. As benchmarking we use some well known nonlinearity tests.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
