Forecast combinations under structural break uncertainty

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single postbreak estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.
Original languageEnglish
Pages (from-to)161 - 175
Number of pages15
JournalInternational Journal of Forecasting
Volume30
Issue number1
DOIs
Publication statusPublished - 2014

Cite this

@article{be7408a9e49a430f960444b89382a5da,
title = "Forecast combinations under structural break uncertainty",
abstract = "This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single postbreak estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.",
author = "Jing Tian and Anderson, {Heather Margot}",
year = "2014",
doi = "10.1016/j.ijforecast.2013.06.003",
language = "English",
volume = "30",
pages = "161 -- 175",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier",
number = "1",

}

Forecast combinations under structural break uncertainty. / Tian, Jing; Anderson, Heather Margot.

In: International Journal of Forecasting, Vol. 30, No. 1, 2014, p. 161 - 175.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Forecast combinations under structural break uncertainty

AU - Tian, Jing

AU - Anderson, Heather Margot

PY - 2014

Y1 - 2014

N2 - This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single postbreak estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.

AB - This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single postbreak estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.

U2 - 10.1016/j.ijforecast.2013.06.003

DO - 10.1016/j.ijforecast.2013.06.003

M3 - Article

VL - 30

SP - 161

EP - 175

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 1

ER -