Adaptive Forecasting in the Presence of Recent and Ongoing Structural Change
AbstractWe 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.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 691.
Date of creation: Mar 2012
Date of revision:
Recent and ongoing structural change; Forecast combination; Robust forecasts;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-03-28 (All new papers)
- NEP-ECM-2012-03-28 (Econometrics)
- NEP-ETS-2012-03-28 (Econometric Time Series)
- NEP-FOR-2012-03-28 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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11/05, Department of Economics, City University London.
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