Chapter 4 Forecast Combinations

Timmermann, Allan

doi:10.1016/s1574-0706(05)01004-9

Cited by 1,232 publications

(733 citation statements)

References 59 publications

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“…One way to employ the full set of available information is to pool the results of several models. 17 The literature has shown that the combination of forecasts often results in an improvement of forecast accuracy compared to univariate benchmark models or to a single modelling strategy (see, Granger and Newbold, 1977;Stock and Watson, 2004;Timmermann, 2006). An additional advantage of model averaging is that it guards against instabilities (Hendry and Clements, 2004) and often results in a more stable and reliable forecasting performance (see, Drechsel and Scheufele, 2012b, before and during the financial crisis).…”

Section: Model Combinationmentioning

confidence: 99%

“…Despite its simplicity, it has been found that the equal weighting scheme is hard to beat in practice (see, Timmermann, 2006) and thus serves a natural benchmark for other more sophisticated weighting schemes. 19…”

Section: Model Combinationmentioning

confidence: 99%

“…We set δ = 0.6 which implies a moderate degree of discounting. 24 A weighting scheme that is often neglected in the literature is based on ranks (viii ) (see, Timmermann, 2006). It is thus closely related to the previous combination scheme, but the weights are assigned according to the model ranks instead of the precise MSFEs.…”

Section: Combination According To the Out-of-sample Performancementioning

confidence: 99%

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Bottom-up or direct? Forecasting German GDP in a data-rich environment

Drechsel

Scheufele

2017

Empir Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: Model Combinationmentioning

confidence: 99%

Section: Model Combinationmentioning

confidence: 99%

Section: Combination According To the Out-of-sample Performancementioning

confidence: 99%

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Bottom-up or direct? Forecasting German GDP in a data-rich environment

Drechsel

Scheufele

2017

Empir Econ

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“…Many research papers have suggested that combining will perform better than individual methods (Clemen, 1989;Clements and Hendry, 1998;de Menezes et al, 2000;Riedel and Gabrys, 2005;Altavilla and De Grauwe, 2006;Timmermann, 2006;Chen and Yang, 2007;Clark and McCracken, 2009), including some applications to electricity demand forecasting (see Taylor and Majithia, 2000;Taylor, 2010). In the context of electricity prices, García-Martos et al (2007) similarly advocate combining, but within a single model class (ARIMA), to deal with specification uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

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“…Indeed, if two or more models have the same predictive ability, and we have no particular interest in choosing one over the other, forecast combination increases efficiency and reduces model risk (see, e.g., Timmermann, 2006).…”

Section: The Model Confidence Set Approachmentioning

confidence: 99%

Multiple Forecast Model Evaluation

Corradi

Distaso²

2012

The Oxford Handbook of Economic Forecasting

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This article focuses on recent developments in the forecasting literature on how to simultaneously control both the overall error rate and the contribution of irrelevant models. As a novel contribution, it derives a general class of superior predictive ability tests, which controls for family-wise error rate (FWER) and the contribution of irrelevant models. The article is organized as follows. Section 2 defines the setup. Section 3 reviews the approaches that control for the conservative FWER. Section 4 considers a general class of tests characterized by multiple joint inequalities. Section 5 presents results allowing for control of the less conservative false discovery rate. Section 6 considers the model confidence set approach and offers a simple alternative that reduces the influence of irrelevant models in the initial set. Section 7 briefly reviews the empirical evidence, while Section 8 concludes.

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Chapter 4 Forecast Combinations

Cited by 1,232 publications

References 59 publications

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Combining day-ahead forecasts for British electricity prices

Multiple Forecast Model Evaluation

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