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1982) and Makridakis and Hibon (2000). Until recently, while the first two approaches often compared their forecasts with various ‘naive’methods selected from the third group, there was little direct comparison ... While Makridakis and Hibon (2000)
www.economics.ox.ac.uk/materials/papers/5748/paper600.pdf- 253k - 18 Apr 2012 - Cached
Clements and Hendry(2001) use that analysis to explain the outcomes of forecasting competitions, where the simplicity of amodel is viewed as essential for success (see e.g., Makridakis and Hibon,
www.economics.ox.ac.uk/materials/papers/5081/paper551.pdf- 161k - 25 May 2011 - Cached
Makridakis and Hibon, 2000) and.
www.economics.ox.ac.uk/materials/papers/14384/paper-779.pdf- 369k - 2 Feb 2016 - Cached
7See Spyros Makridakis and Michelle Hibon “The M3-competition: Results, conclusions and implications”,InternationalJournal of Forecasting, 16, 451–476, and Robert Fildes and Keith Ord “Forecasting competitions–their role in improving.
www.economics.ox.ac.uk/materials/papers/4864/paper530.pdf- 145k - 8 Feb 2011 - Cached
1. Forecasting Electricity Smart Meter Data Using. Conditional Kernel Density Estimation. Siddharth Arora†and James W. Taylor. Saїd Business School,. University of Oxford, Park End Street, Oxford, OX1 1HP, U.K. OMEGA - The International Journal
people.maths.ox.ac.uk/arora/SmartMeterManuscript_OMEGA.pdf- 890k - 28 Aug 2014 - Cached
The large empirical forecasting competitions, such as Makridakis, Andersen, Carbone, Fildes et al.(1982) and Makridakis and Hibon (2000), reviewed respectively by Fildes and Makridakis (1995) andClements and Hendry (2001a), produce
www.economics.ox.ac.uk/materials/papers/13019/paper674.pdf- 960k - 1 Oct 2013 - Cached
However, there is little agreement on which approaches performbest on a forecasting criterion: see Makridakis and Hibon (2000) and Fildes and Ord (2002) for evidencefrom forecast competitions.
www.economics.ox.ac.uk/materials/working_papers/4982/selecting-a-model-for-forcasting-861.pdf- 1008k - 9 Nov 2018 - Cached
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