A Simple Explanation of the Forecast Combination Puzzle*

Smith, Jeremy; Wallis, Kenneth F.

doi:10.1111/j.1468-0084.2008.00541.x

Cited by 270 publications

(158 citation statements)

References 18 publications

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“…Several studies have shown that, due to the effect of finite-sample error in estimating the combining weights, an equally weighted mean is often the best choice (Makridakis and Winkler, 1983;Clemen, 1989;Watson, 2001, 2004;Smith and Wallis, 2009). We follow this conclusion and in the rest of the paper we assume θ k = 1/K.…”

Section: Combining Forecastsmentioning

confidence: 99%

“…Methods of increasing sophistication followed the simple adaptive time series approach of Bates and Granger (1969), including Bayesian (Bunn, 1975(Bunn, , 1977, and econometric (Granger and Ramanathan, 1984), as well as extensions to large data sets Watson, 2001, 2004), but, for robust forecasting, it has appeared hard to improve upon simple averaging (Makridakis and Winkler, 1983;Clemen, 1989;Watson, 2001, 2004;Smith and Wallis, 2009). We therefore do not address the question of developing combining methods to improve on simple averaging.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Combining Forecastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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“…We have few observations, so the so-called "forecast combination puzzle" (the fact that the sample average of forecasts gives better forecasting results than more sophisticated weighting schemes) might arise. See, for instance, Smith and Wallis (2009) and Aiolfi, Capistrán and Timmermann (2011). We would like to suggest forecast combination procedures that might work better in situations in which the sample is quite short.…”

Section: Mexico: Combining Monthly Inflation Predictions From Surveysmentioning

confidence: 99%

Mexico : Combining monthly inflation predictions from surveys

Poncela¹,

Guerrero²,

Islas³

et al. 2014

CEPAL Review

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We examine the problem of combining Mexican inflation predictions or projections provided by a biweekly survey of professional forecasters. Consumer price inflation in Mexico is measured twice a month. We consider several combining methods and advocate the use of dimension reduction techniques whose performance is compared with different benchmark methods, including the simplest average prediction. Missing values in the database are imputed by two different databased methods. The results obtained are basically robust to the choice of the imputation method. A preliminary analysis of the data was based on its panel data structure and showed the potential usefulness of using dimension reduction techniques to combine the experts' predictions. The main findings are: the first monthly predictions are best combined by way of the first principal component of the predictions available; the best second monthly prediction is obtained by calculating the median prediction and is more accurate than the first one. KEYWORDS

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“…With regard to forecast combination, Capistran and Timmermann (2009), as well as Timmermann (2004, 2005), estimate combination weights by minimizing a given loss function, ensuring that the weights converge to those minimizing expected loss. Wallis (2005) proposes combining forecasts using a finite mixture distribution, and Smith and Wallis (2009) suggest the use of simple averages. With regard to rationality assessment, Elliott, Komunjer and Timmermann (2008) test whether forecasters taking part in the SPF are rational for some parameterization of a flexible loss function.…”

Section: Motivationmentioning

confidence: 99%

A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance

Corradi

Swanson

2012

Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis

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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 AbstractIn recent years, an impressive body or research on predictive accuracy testing and model comparison has been published in the econometrics discipline. Key contributions to this literature include the paper by Diebold and Mariano (DM: 1995) that sets the groundwork for much of the subsequent work in the area, West (1996) who considers a variant of the DM test that allows for parameter estimation error in certain contexts, and White (2000) who develops testing methodology suitable for comparing many models. In this chapter, we begin by reviewing various key testing results in the extant literature, both under vanishing and non-vanishing parameter estimation error, with focus on the construction of valid bootstrap critical values in the case of non-vanishing parameter estimation error, under recursive estimation schemes, drawing on Corradi and Swanson (2007a). We then review recent extensions to the evaluation of multiple confidence intervals and predictive densities, for both the case of a known conditional distribution (Corradi and Swanson 2006a,b) and of an unknown conditional distribution (Corradi and Swanson 2007b). Finally, we introduce a novel approach in which forecast combinations are evaluated via the examination of the quantiles of the expected loss distribution. More precisely, we compare models looking at cumulative distribution functions (CDFs) of prediction errors, for a given loss function, via the principle of stochastic dominance; and we choose the model whose CDF is stochastically dominated, over some given range of interest.JEL classification: C22, C51.

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A Simple Explanation of the Forecast Combination Puzzle*

Cited by 270 publications

References 18 publications

Combining day-ahead forecasts for British electricity prices

Combining day-ahead forecasts for British electricity prices

Mexico : Combining monthly inflation predictions from surveys

A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance

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