Nonparametric bootstrap test for autoregressive additive models

Luca Bagnato; Antonio Punzo

Nonparametric bootstrap test for autoregressive additive models

Risultato della ricerca: Contributo in rivista › Articolo in rivista › peer review

Abstract

In this paper a procedure for testing additivity in nonlinear time series analysis is provided. The method is based on: Generalized Likelihood Ratio Test(Zhang, 2001), Volterra expansion (Chen et al., 1995), and nonparametric conditional bootstrap (Jianqing and Qiwei, 2003). Investigations about performance (in terms of empirical size and power), and comparisons with other additivity tests proposed by Chen et al. (1995), are made recurring to Monte Carlo simulations.

Lingua originale	English
pagine (da-a)	359-370
Numero di pagine	12
Rivista	Statistics in Transition
Volume	10
Stato di pubblicazione	Pubblicato - 2009

Keywords

Additive models
Generalized Likelihood Ratio Test

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AU - Bagnato, Luca

AU - Punzo, Antonio

PY - 2009

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AB - In this paper a procedure for testing additivity in nonlinear time series analysis is provided. The method is based on: Generalized Likelihood Ratio Test(Zhang, 2001), Volterra expansion (Chen et al., 1995), and nonparametric conditional bootstrap (Jianqing and Qiwei, 2003). Investigations about performance (in terms of empirical size and power), and comparisons with other additivity tests proposed by Chen et al. (1995), are made recurring to Monte Carlo simulations.

KW - Additive models

KW - Generalized Likelihood Ratio Test

KW - Additive models

KW - Generalized Likelihood Ratio Test

UR - http://hdl.handle.net/10807/41325

M3 - Article

VL - 10

SP - 359

EP - 370

JO - Statistics in Transition

JF - Statistics in Transition

SN - 1234-7655

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