SELECTION OF AN ESTIMATION WINDOW IN THE PRESENCE OF DATA REVISIONS AND RECENT STRUCTURAL BREAKS
Hännikäinen, Jari (2016)
Tampereen yliopisto
2016
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Julkaisun pysyvä osoite on
http://urn.fi/URN:ISBN:978-952-03-0308-2
http://urn.fi/URN:ISBN:978-952-03-0308-2
Kuvaus
Reprinted.
Tiivistelmä
In this paper, we analyze the forecasting performance of a set of widely used
window selection methods in the presence of data revisions and recent structural
breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation
show that the expanding window estimator often yields the most accurate forecasts
after a recent break. It performs well regardless of whether the revisions
are news or noise, or whether we forecast first-release or final values. We find
that the differences in the forecasting accuracy are large in practice, especially
when we forecast inflation after the break of the early 1980s.
window selection methods in the presence of data revisions and recent structural
breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation
show that the expanding window estimator often yields the most accurate forecasts
after a recent break. It performs well regardless of whether the revisions
are news or noise, or whether we forecast first-release or final values. We find
that the differences in the forecasting accuracy are large in practice, especially
when we forecast inflation after the break of the early 1980s.