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Dynamics of Model Overfitting Measured in terms of Autoregressive Roots
Author(s) -
Granger Clive W. J.,
Jeon Yongil
Publication year - 2006
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.1467-9892.2006.00468.x
Subject(s) - autoregressive model , overfitting , akaike information criterion , mathematics , star model , setar , nonlinear autoregressive exogenous model , econometrics , series (stratigraphy) , bayesian information criterion , bayesian probability , information criteria , multivariate statistics , statistics , time series , model selection , autoregressive integrated moving average , artificial intelligence , computer science , paleontology , biology , artificial neural network
Abstract. One method of describing the properties of a fitted autoregressive model of order p is to show the p roots that are implied by the lag operator. Considering autoregressive models fitted to 215 US macro series, with lags chosen by either the Bayesian or Schwarz information criteria or Akaike information criteria, the roots are found to constitute a distinctive pattern. Later analysis suggests that much of this pattern occurs because of overfitting of the models. An extension of the results shows that they have some practical multivariate time‐series modelling implications.