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.

Item Type:	Refereed Article
Keywords:	Bias–variance trade-offs, Choice of estimation windows, Inflation forecasts, Parameter shifts, Reversed order CUSUM tests, Weighted forecats
Research Division:	Economics
Research Group:	Econometrics
Research Field:	Economic Models and Forecasting
Objective Division:	Economic Framework
Objective Group:	Macroeconomics
Objective Field:	Monetary Policy
Author:	Tian, J (Dr Jing Tian)
ID Code:	92792
Year Published:	2014
Web of Science® Times Cited:	10
Deposited By:	Tasmanian School of Business and Economics
Deposited On:	2014-06-27
Last Modified:	2018-03-07
Downloads:	0