Nonparametric bootstrap test for autoregressive additive models

Luca Bagnato, Antonio Punzo

Research output: Contribution to journalArticlepeer-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.
Original languageEnglish
Pages (from-to)359-370
Number of pages12
JournalStatistics in Transition
Volume10
Publication statusPublished - 2009

Keywords

  • Additive models
  • Generalized Likelihood Ratio Test

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