2011
DOI: 10.1016/j.ijforecast.2010.01.012
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Forecast combination through dimension reduction techniques

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Cited by 54 publications
(39 citation statements)
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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%
“…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%
“…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%
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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). …”
mentioning
confidence: 99%