Model Confidence Sets and forecast combination

Samuels, Jon D.; Sekkel, Rodrigo

doi:10.1016/j.ijforecast.2016.07.004

Cited by 49 publications

(41 citation statements)

References 24 publications

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“…Hansen et al (, ) apply an MCS procedure in the context of volatility and forecasting models. Samuels and Sekkel () use the MCS to select a subset of models prior to averaging the resulting forecasts.…”

Section: Introductionmentioning

confidence: 99%

Model Confidence Bounds for Variable Selection

Luo

Ferrari

et al. 2019

Biometrics

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In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool—the model uncertainty curve (MUC)—is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Model Confidence Bounds for Variable Selection

Luo

Ferrari

et al. 2019

Biometrics

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“…All of this information is used by the Brazilian Central Bank to gauge its monetary policy. Finally, following Samuels and Sekkel (2017), we use a forecast combination strategy based on the model confidence sets proposed by Hansen, Lunde, and Nason (2011). The idea is to compute the average of the forecasts from the models included in a given confidence set.…”

Section: Introductionmentioning

confidence: 99%

Real-time inflation forecasting with high-dimensional models: The case of Brazil

García

Medeiros

Vasconcelos

2017

International Journal of Forecasting

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“…The ensemble model extracts the advantages of each individual model and has a higher prediction accuracy. Therefore, for constructing an adaptive ensemble model with stronger generalization ability and higher forecasting accuracy, determining the combined weight of every single model is the focus of the ensemble model [61,62]. The linear combination of predictive models can improve the predictive power of a single model, but the flexibility in assigning weights is relatively poor, thus affecting the adaptability of the ensemble model [63,64].…”

Section: Ensemble-based Methodsmentioning

confidence: 99%

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Zhao

Yao

et al. 2019

Energies

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Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

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Model Confidence Sets and forecast combination

Cited by 49 publications

References 24 publications

Model Confidence Bounds for Variable Selection

Model Confidence Bounds for Variable Selection

Real-time inflation forecasting with high-dimensional models: The case of Brazil

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

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