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Combining Forecasting Procedures: Some Theoretical Results

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Author Info
Yang, Yuhong
Abstract

We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under distributional assumptions on the future given the current outside information and the past observations. The risk bounds show that the combined forecast automatically achieves the best performance among the candidate procedures up to a constant factor and an additive penalty term. In terms of the rate of convergence, the combined forecast performs as well as if the best candidate forecasting procedure were known in advance.Empirical studies suggest that combining procedures can sometimes improve forecasting accuracy over the original procedures. Risk bounds are derived to theoretically quantify the potential gain and price of linearly combining forecasts for improvement. The result supports the empirical finding that it is not automatically a good idea to combine forecasts. Indiscriminate combining can degrade performance dramatically as a result of the large variability in estimating the best combining weights. An automated combining method is shown in theory to achieve a balance between the potential gain and the complexity penalty (the price of combining), to take advantage (if any) of sparse combining, and to maintain the best performance (in rate) among the candidate forecasting procedures if linear or sparse combining does not help.

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File URL: http://journals.cambridge.org/abstract_S0266466604201086
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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 20 (2004)
Issue (Month): 01 (February)
Pages: 176-222
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:cup:etheor:v:20:y:2004:i:01:p:176-222_20

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  1. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics. [Downloadable!]
  2. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy. [Downloadable!]
    Other versions:
  3. Sancetta, A., 2007. "Online Forecast Combination for Dependent Heterogeneous Data," Cambridge Working Papers in Economics 0718, Faculty of Economics, University of Cambridge. [Downloadable!]
  4. Laurence Fung & Ip-wing Yu, 2008. "Predicting Stock Market Returns by Combining Forecasts," Working Papers 0801, Hong Kong Monetary Authority. [Downloadable!]
  5. Pötscher, Benedikt M., 2006. "The Distribution of Model Averaging Estimators and an Impossibility Result Regarding Its Estimation," MPRA Paper 73, University Library of Munich, Germany, revised Jul 2006. [Downloadable!]
  6. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society. [Downloadable!]
  7. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112. [Downloadable!]
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