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Adaptive Order Determination for Constructing Time Series Forecasting Models

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  • Yongli Zhang
  • Sergio Koreisha

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.

Suggested Citation

  • Yongli Zhang & Sergio Koreisha, 2015. "Adaptive Order Determination for Constructing Time Series Forecasting Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(22), pages 4826-4847, November.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:22:p:4826-4847
    DOI: 10.1080/03610926.2013.800881
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    Cited by:

    1. Kley, Tobias & Preuss, Philip & Fryzlewicz, Piotr, 2019. "Predictive, finite-sample model choice for time series under stationarity and non-stationarity," LSE Research Online Documents on Economics 101748, London School of Economics and Political Science, LSE Library.

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