Adaptive Order Determination for Constructing Time Series Forecasting Models

Yongli Zhang and Sergio Koreisha

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 22, 4826-4847

Abstract: In time series modeling consistent criteria like Bayesian Information Criterion (BIC) outperform in terms of predictability loss-efficient criteria like Akaike Information Criterion (AIC) when data are generated by a finite-order autoregressive process, and the reverse is true when data are generated by an infinite-order autoregressive process. Since in practice we don’t know the data-generating process, it is useful to have an adaptive criterion that behaves as either a consistent or just as a loss-efficient criterion, whichever performs better. Here we derive such a criterion. Moreover, our criterion is adaptive to effective sample sizes and not sensitive to maximum a priori determined order limits.

Date: 2015
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DOI: 10.1080/03610926.2013.800881

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