Forecast combination through dimension reduction techniques

Poncela, Pilar; Rodríguez, Julio; Sánchez-Mangas, Rocío; Senra, Eva

doi:10.1016/j.ijforecast.2010.01.012

Cited by 53 publications

(39 citation statements)

References 24 publications

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“…Empirically, the improvements of using forecast combination instead of a "best" model have been shown for different types of models (for instance, see [23][24][25]) and in various research areas [15,26]. However, [1] points out that forecast combination techniques have not been fully exploited for electricity prices.…”

Section: Forecast Combinationmentioning

confidence: 99%

Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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Abstract:In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.

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Section: Forecast Combinationmentioning

confidence: 99%

Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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“…The forecast framework is based on the dimension reduction techniques proposed by Poncela and others (2011), which allow us to obtain a single, more accurate forecast of inflation rather than several individual forecasts. Dimension reduction techniques are used to extract the common information contained in the experts' forecasts This work benefited from financial support from an inter-university cooperation initiative, uam-banco santander, with Latin America.…”

Section: Mexico: Combining Monthly Inflation Predictions From Surveysmentioning

confidence: 99%

“…In the first monthly predictions, the contribution of the aggregate effect is higher in the forecast error (i.e., lower in the forecast) and thus, more sophisticated combination schemes, such as the dimension reduction techniques shown in the next section, may have a chance to surpass the simple methods. Poncela and Senra (2006) and extended in Poncela and others (2011). The key insight is to see the forecast combination as a way to reduce the dimension from N (the number of forecasters at each period of time) to a single one.…”

Section: Panel Data Analysismentioning

confidence: 99%

“…A nonstationary pc was proposed by Lee and Carter (1992). Poncela and others (2011) demonstrated that simple factor models (fms) are better suited for forecast combination than more complicated factor schemes, probably because the number of parameters estimated to form the weights for the combination is lower than in more complex factor alternatives. In particular, they found that static fms performed quite well.…”

Section: Principal Componentsmentioning

confidence: 99%

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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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“…Our approach may be related to the work on forecast combination (Bates & Granger, 1969;Deutsch et al, 1994;Guidolin & Timmermann, 2009;Granger & Ramanathan, 1984;Hoogerheide, Kleijn, Ravazzolo, van Dijk, & Verbeek, 2010, LeSage & Magura, 1992, dimension reduction (Poncela, Rodríguez, Sánchez-Mangas, & Senra, 2011); periodic ARMA models (Basawa & Lund, 2001;Boswijk & Franses, 1996;Franq et al, 2011;Franses & Paap, 2004;Herwartz, 1999;Jones & Brelsford, 1967;Novales & Flores de Frutos, 1997;Osborn & Smith, 1994), panel-data methods (Issler & Lima, 2009). …”

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confidence: 99%

Split-then-Combine Method for out-of-sample Combinations of Forecasts

Arroyo¹,

Fernández²

2014

JBAR

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Relative forecast performance of forecast units may periodically evolve over time. Therefore, it is desirable to take into account their forecast periodicity when forming forecast combinations. When dealing with small samples and small number of models, using panels is an efficient way of pulling out the additional information provided by that periodicity in the data. We capture this periodic information with different weights at different periods that we then keep in the out-of-sample combination. As in the simple average, we do not estimate weights, but instead compute them from panels of forecasts taken as given data. Empirical and bootstrap exercises illustrate the superiority of this method over fixed weight schemes.JEL Codes: C11, C22, C53

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Forecast combination through dimension reduction techniques

Cited by 53 publications

References 24 publications

Electricity Price Forecasting by Averaging Dynamic Factor Models

Electricity Price Forecasting by Averaging Dynamic Factor Models

Mexico : Combining monthly inflation predictions from surveys

Split-then-Combine Method for out-of-sample Combinations of Forecasts

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