Adaptive forecasting in the presence of recent and ongoing structural change

Working papers set out research in progress by our staff, with the aim of encouraging comments and debate.
Published on 28 March 2014

Working Paper No. 490
By Liudas Giraitis, George Kapetanios and Simon Price

We consider time series forecasting in the presence of ongoing structural change where both the time-series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found also to be useful in the presence of ongoing structural change in the forecast period. A crucial issue is how to select the degree of downweighting, usually defined by an arbitrary tuning parameter. We make this choice data-dependent by minimising forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 97 US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well.

PDF Adaptive forecasting in the presence of recent and ongoing structural change 

Other papers

Give your feedback

Was this page useful?
Yes
No
Add your details...