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Theses

Modèles non linéaires et prévision

Abstract : The interest of non-linear models is, on the one hand, to better take into account non-linearities characterizing themacroeconomic and financial series and, on the other hand, to get richer information in forecast. At this level,originality intervals (asymmetric and / or discontinuous) and forecasts densities (asymmetric and / or multimodal)offered by this new modelling form suggests that improving forecasts according to linear models is possible and thatwe should have enough powerful tests of evaluation to check this possible improvement. Such tests usually meanchecking distributional assumptions on violations and probability integral transform processes respectively associatedto each of these forms of forecast. In this thesis, we have adapted the GMM framework based on orthonormalpolynomials designed by Bontemps and Meddahi (2005, 2012) to test for some probability distributions, an approachalready adopted by Candelon et al. (2011) in the context of backtesting Value-at-Risk. In addition to the simplicity androbustness of the method, the tests we have developed have good properties in terms of size and power. The use of ournew approach in comparison of linear and non-linear models in an empirical analysis confirmed the idea according towhich the former are preferred if the goal is the calculation of simple point forecasts while the latter are moreappropriated to report the uncertainty around them.
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Submitted on : Monday, December 2, 2013 - 5:07:08 PM
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Jaouad Madkour. Modèles non linéaires et prévision. Economies et finances. Université d'Orléans, 2013. Français. ⟨NNT : 2013ORLE0501⟩. ⟨tel-00912861⟩

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