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SELECTING ORDER FOR GENERAL AUTOREGRESSIVE MODELS BY MINIMUM DESCRIPTION LENGTH
Author(s) -
Huang Dawei
Publication year - 1990
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1990.tb00045.x
Subject(s) - autoregressive model , mathematics , star model , convergence (economics) , order (exchange) , setar , constraint (computer aided design) , polynomial , statistics , mathematical optimization , autoregressive integrated moving average , mathematical analysis , time series , geometry , finance , economics , economic growth
. In this paper we put forward some criteria for estimating the order for general autoregressive (AR) models (i.e. AR models without any constraint about the roots of the characteristic polynomial) according to the minimum description length. Different criteria are given for different kinds of AR models because the convergence rates are different. It is proved that all the estimates for the order are strongly consistent.