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A machine learning dynamic switching approach to forecasting when there are structural breaks

Abstract:

Forecasting economic indicators is an important task for analysts. However, many indicators suffer from structural breaks leading to forecast failure. Methods that are robust following a structural break have been proposed in the literature but they come at a cost: an increase in forecast error variance. We propose a method to select between a set of robust and non-robust forecasting models. Our method uses time-series clustering to identify possible structural breaks in a time series, and th...

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Publication status:
Published

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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Oxford college:
Magdalen College
Role:
Author
ORCID:
0000-0001-9325-8024
Publisher:
University of Oxford Publisher's website
Article number:
950
Series:
Department of Economics Discussion Paper Series
Publication date:
2021-10-13
Paper number:
950
Language:
English
Keywords:
Pubs id:
1202539
Local pid:
pubs:1202539
Deposit date:
2021-10-13

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