Model order selection for cascade autoregressive (CAR) models
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Date
2021
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Abstract
In recent years, Cascade Autoregression (CAR) models enjoy increasing popularity in applied econometrics.
This is due to the fact that they are able to approximate both short- and long-memory processes and are easy
to implement. However, their model order, namely the timing of the steps, relies on ad-hoc decisions rather
than being data-driven. In this paper, techniques for model order selection of CAR models in finite samples
are presented. The approaches are evaluated in an extensive simulation study, as well as in an empirical
application. The results suggest that model order selection may provide gains in both in- and out-of-sample
performance.
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Keywords
cascade models, cross-validation, forecasting, time-series modeling, long-memory, model selection, HAR