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Searching for the DGP when forecasting : is it always meaningful for small samples?

Andersson, Jonas
Journal article, Peer reviewed
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URI
http://hdl.handle.net/11250/163556
Date
2006
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Abstract
In this paper the problem of choosing a univariate forecasting model for small samples is

investigated. It is shown that, a model with few parameters, frequently, is better than a model

which coincides with the data generating process (DGP) (with estimated parameter values).

The exponential smoothing algorithms are, once more, shown to perform remarkably well for

some types of data generating processes, in particular for short-term forecasts. All this is

shown by means of Monte Carlo simulations and a time series of realized volatility from the

CAC40 index. The results speaks in favour of a negative answer to the question posed in the

title of this paper.
Publisher
Economics Bulletin
Journal
Economics Bulletin

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