Nonlinear additive ARX models

Rong Chen, Ruey S. Tsay

Research output: Contribution to journalArticlepeer-review

113 Scopus citations

Abstract

We consider in this article a class of nonlinear additive autoregressive models with exogenous variables for nonlinear time series analysis and propose two modeling procedures for building such models. The procedures proposed use two backfitting techniques (the ACE and BRUTO algorithms) to identify the nonlinear functions involved and use the methods of best subset regression and variable selection in regression analysis to determine the final model. Simulated and real examples are used to illustrate the analysis.

Original languageEnglish (US)
Pages (from-to)955-967
Number of pages13
JournalJournal of the American Statistical Association
Volume88
Issue number423
DOIs
StatePublished - Sep 1993
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Additivity
  • Alternating conditional expectation (ACE) algorithm
  • BRUTO algorithm
  • Best subset regression
  • River flow
  • Time series
  • Variable selection

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