SELECTION OF AN ESTIMATION WINDOW IN THE PRESENCE OF DATA REVISIONS AND RECENT STRUCTURAL BREAKS
Hännikäinen, Jari (2013)
Tampereen yliopisto
2013
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Julkaisun pysyvä osoite on
http://urn.fi/URN:ISBN:978-951-44-9336-2
http://urn.fi/URN:ISBN:978-951-44-9336-2
Kuvaus
Päivitetty 17.9.2015
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 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 fi rst-release or final values. We fi nd that the diff erences in the forecasting accuracy are large in practice, especially when we forecast GDP deflator growth 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 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 fi rst-release or final values. We fi nd that the diff erences in the forecasting accuracy are large in practice, especially when we forecast GDP deflator growth after the break of the early 1980s.