Estimating the variance of a combined forecast: Bootstrap-based approach

Hounyo, Ulrich; Lahiri, Kajal

doi:10.1016/j.jeconom.2021.09.011

Cited by 5 publications

(4 citation statements)

References 62 publications

(119 reference statements)

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“…This is related to the work of Gneiting et al (2005). It may also be challenging, however, as indicated by more recent work such as Hounyo and Lahiri (2021).…”

Section: Concluding Remarks and Directions For Future Researchmentioning

confidence: 98%

On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

Diebold¹,

Shin²,

Zhang³

2022

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We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts' probability mass from the centers to the tails, correcting for overconfidence.

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“…This is related to the work of Gneiting et al (2005). It may also be challenging, however, as indicated by more recent work such as Hounyo and Lahiri (2021).…”

Section: Concluding Remarks and Directions For Future Researchmentioning

confidence: 98%

On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

Diebold¹,

Shin²,

Zhang³

2022

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“…Por tanto, se puede afirmar que, al utilizar el remuestreo del componente residual de la serie temporal mediante la técnica MBB, las previsiones de la producción de energía FV mejoran consistentemente. Ello se relaciona con los estudios de [27], [28], donde el autor demostró un alto poder predictivo y menos incertidumbre de pronóstico al usar la técnica M BB utilizados en la previsión de energía eólica. De igual manera, el resultado coincide con los de otros estudios que utilizan el enfoque bootstrap con el modelo de promedio móvil integrado autorregresivo, que se utilizó para el pronóstico mensual de cargas eléctricas [32].…”

Section: Intervalo [S Eman As ]unclassified

“…Algunos estudios mostraron un alto poder predictivo y menos errores en la técnica de arranque cuando esta última se utilizó con otros métodos de previsión; por ejemplo, se referencia un algoritmo de bosque aleatorio en [26]. En [27] se usa las técnicas de bootstrap, junto con modelos de promedio ponderado con pesos fijos. De la mis ma forma, en [28] se presenta un estudio de la combinación de las técnicas Bootstrap relacionando métodos del promedio aritmético y el promedio ponderado para la previsión de la energía eólica.…”

Section: Introductionunclassified

Optimization of Solar PV Power Forecasting Using Bootstrap Techniques and the Feed-Forward Neural Network Model

Zarate-Perez¹,

Palumbo²,

Motta³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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The outbreak of the COVID-19 disease has exerted a deep and extensive influence on the energy sector. The work modality and lifestyle caused by the confinement policy have increased electricity consumption in the residential sector. In such a way that the application of photovoltaic solar energy (PV) is rapidly evolving to mitigate the problems caused. However, due to the variability and uncertainty of solar irradiance, several technical challenges are created to produce PV energy. To reduce these adverse effects, forecasting of energy production at multiple scales is used. In this sense, the objective of this study is to determine the forecast performance of a hybrid model through the application of a Feed-Forward Neural Network (FFNN), together with the application of the moving block bootstrap technique (MBB), using the real data of the production of a PV system. The results show that the FFNN method combined with MBB techniques consistently outperform the original FFNN method in terms of forecast accuracy. That is, the original model presents a performance of 4.48% percentage forecast error (MAPE), compared to 3.14% fo r the proposed hybrid model. Finally, through the Ljung-Box test it isshown that the results are not correlated; therefore, the recommended model is validated.

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“…In the context of estimating variance of a combined forecast based on different models, we have proposed an appropriate bootstrap procedure to accommodate serial correlation and cross‐sectional correlations, see Hounyo and Lahiri (2021).…”

Section: Test For Differences In Forecaster Performancementioning

confidence: 99%

Are Some Forecasters Really Better than Others? A Note*

Hounyo¹,

Lahiri²

2022

J of Money Credit Banking

Self Cite

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In this paper, we use bootstrap approach to test the null hypothesis that all forecasters in the U.S. Surveys of Professional Forecasters (SPF) have equal ability. Our bootstrap procedure captures any potential cross-sectional and serial correlation in the forecast errors while preserving the unbalanced nature of the panel data. Once we account for the presence of cross-sectional and serial correlation in the forecast errors while resampling, we find convincing evidence that some individuals really are better than others-this is in sharp contrast to the findings of D' Agostino et al. (2012).

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Estimating the variance of a combined forecast: Bootstrap-based approach

Cited by 5 publications

References 62 publications

On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

Optimization of Solar PV Power Forecasting Using Bootstrap Techniques and the Feed-Forward Neural Network Model

Are Some Forecasters Really Better than Others? A Note*

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