Forecasting with Universal Approximators and a Learning Algorithm

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  • Institut for Økonomi
This paper applies three universal approximators for
forecasting. They are the Artificial Neural Networks, the Kolmogorov-
Gabor polynomials, as well as the Elliptic Basis Function Networks.
Even though forecast combination has a long history in
econometrics focus has not been on proving loss bounds for the
combination rules applied. We apply the Weighted Average Algorithm
(WAA) of Kivinen and Warmuth (1999) for which such loss
bounds exist. Specifically, one can bound the worst case performance
of the WAA compared to the performance of the best single
model in the set of models combined from. The use of universal
approximators along with a combination scheme for which explicit
loss bounds exist should give a solid theoretical foundation to the
way the forecasts are performed. The practical performance will
be investigated by considering various monthly postwar macroeconomic
data sets for the G7 as well as the Scandinavian countries.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider27
StatusUdgivet - 2009

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