Arbeitspapier

Adaptive forecasting in the presence of recent and ongoing structural change

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 to be also 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 minimizing forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 191 UK and US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well.

Language
Englisch

Bibliographic citation
Series: Working Paper ; No. 691

Classification
Wirtschaft
Econometric and Statistical Methods and Methodology: General
Econometric Modeling: Other
Subject
Recent and ongoing structural change
Forecast combination
Robust forecasts

Event
Geistige Schöpfung
(who)
Giraitis, Liudas
Kapetanios, George
Price, Simon
Event
Veröffentlichung
(who)
Queen Mary University of London, School of Economics and Finance
(where)
London
(when)
2012

Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Giraitis, Liudas
  • Kapetanios, George
  • Price, Simon
  • Queen Mary University of London, School of Economics and Finance

Time of origin

  • 2012

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