@article{
 title = {Euro area GDP forecasting using large survey datasets A random forest approach},
 type = {article},
 year = {2015},
 keywords = {Business and Consumer Survey data,GDP short-term forecasting,Random Forests,Variables selection},
 pages = {22},
 websites = {https://unstats.un.org/unsd/nationalaccount/workshops/2010/moscow/AC223-S73Bk4.PDF},
 id = {a6e90cff-3edd-3b02-b60e-5e7ed2941e25},
 created = {2018-06-25T18:43:51.946Z},
 accessed = {2018-06-25},
 file_attached = {false},
 profile_id = {35f49df4-e9cf-391e-a76a-8e9e316c42aa},
 group_id = {ee11d323-dd56-3515-8742-9b1e2f0496f7},
 last_modified = {2018-06-30T13:47:48.034Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {0f486643-3d08-4632-8e8e-e53433d903a9,9f870d1a-3967-406b-94fb-b8524c625bf1},
 private_publication = {false},
 abstract = {Recent works in the econometric literature consider the problem of efficiently summarising a large set of variables and using this summary for a variety of purposes, including forecasts (Stock and Watson, 2002; Forni et al., 2005; Giannone et al., 2008; for a wide review, see Eklund and Kapetanios, 2008). Factor analysis combined with linear modelling has usually been the main tool used for this task. This paper presents a new statistical approach to forecasting macro-economic aggregates, based on the Random Forests technique, originally developed as a learning classification tool (Breiman, 2001). This technique can handle a very large number of input variables without overfitting and is known to enjoy good prediction properties and to be robust to noise.},
 bibtype = {article},
 author = {Biau, Olivier and D 'elia, Angela}
}

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