Searching for the DGP when forecasting : is it always meaningful for small samples?
Journal article, Peer reviewed

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Date
2006Metadata
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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.