Robust forecast combinations

Wei, Xiaoqiao; Yang, Yuhong

doi:10.1016/j.jeconom.2011.09.035

Cited by 29 publications

(25 citation statements)

References 36 publications

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“…When the conditional mean of y i is known to stay in certain range and the related forecasts are relatively restricted, the condition holds. See Subsection 3.1 of Wei & Yang [12] for more discussions on this condition.…”

Section: Conditionsmentioning

confidence: 99%

“…Technically, if the forecast errors are assumed to follow a normal or a double-exponential distribution with zero mean, then the conditional probability density functions used in the combining process of the AFTER scheme can be estimated relatively easily for all of the candidate forecasters, because the estimation of the conditional scale parameters is straightforward (see, e.g., Zou & Yang [11] and Wei & Yang [12], for more details). However, this is not true if a scaled t-distribution is assumed.…”

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confidence: 99%

“…For example, Sancetta [17] assumed that the tails of the target variables are no heavier than exponential decays, which restricts the heaviness of the tails of the forecast errors. Wei & Yang [12] designed a method for errors heavier than the normal distributions, but not heavier than the double-exponential distributions. More recently, Cheng & Yang [18] advocate the incorporation of a smooth surrogate of the L 0 -loss in the performance measure for weighting to reduce the occurrence of outlier forecasts.…”

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confidence: 99%

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Forecast Combination under Heavy-Tailed Errors

2015

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Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student's t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.

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Section: Conditionsmentioning

confidence: 99%

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Forecast Combination under Heavy-Tailed Errors

2015

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“…and Yang (2012) noted that with alternative forecasts being similar and stable, the BG approach tends to be unnecessarily aggressive, and AFTER, in these situations, can perform better. On the other hand, when the best forecaster changes over time and unstable, the gradient-based method for improvement as suggested by MLS can be advantageous.…”

Section: Introductionmentioning

confidence: 99%

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This paper focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012. We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969)

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Machine Learning and Forecast Combination in Incomplete Panels

2013

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Corn Cash Price Forecasting

Xu¹

2020

American J Agri Economics

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We examine the forecasting problem in a data set of daily corn cash prices from seven states: Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We assess thirty individual time series models and ten combined forecasts based on six trimming strategies across three out‐of‐time evaluation periods, seven horizons, and two systems (bi‐ and multivariate). Using the unrestricted least squares without an intercept to estimate combination weights of individual models without trimming arrives at the lowest root mean squared errors across all evaluation dimensions. Incorporating local cash prices in a model could benefit accuracy, especially for relatively longer out‐of‐time evaluation periods and forecast horizons. Our results suggest model recalibration frequency no lower than one month. Discussions of empirical findings at a more granular level also are presented, including comparisons of individual time series models and those of combined forecasts based on different trimming strategies. The forecasting framework shown here is not difficult to implement and has potential of generalizing to other commodities. This article thus contributes to fulfilling different forecasting users' information needs for decision making under various circumstances.

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Robust forecast combinations

Cited by 29 publications

References 36 publications

Forecast Combination under Heavy-Tailed Errors

Forecast Combination under Heavy-Tailed Errors

Machine Learning and Forecast Combination in Incomplete Panels

Corn Cash Price Forecasting

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