Multi-step forecasting in the presence of breaks
Hännikäinen, Jari (2014)
Tampereen yliopisto
2014
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http://urn.fi/URN:ISBN:978-951-44-9497-0
http://urn.fi/URN:ISBN:978-951-44-9497-0
Tiivistelmä
This paper analyzes the relative performance of multi-step forecasting methods
in the presence of breaks and data revisions. Our Monte Carlo simulations
indicate that the type and the timing of the break affect the relative accuracy of
the methods. The iterated method typically performs the best in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real-time U.S. output and inflation series shows that the alternative multi-step methods only episodically improve upon the iterated method.
in the presence of breaks and data revisions. Our Monte Carlo simulations
indicate that the type and the timing of the break affect the relative accuracy of
the methods. The iterated method typically performs the best in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real-time U.S. output and inflation series shows that the alternative multi-step methods only episodically improve upon the iterated method.