ESTIMATION AND ASYMPTOTIC THEORY FOR A NEW CLASS OF MIXTURE MODELS
In this paper a new model of mixture of distributions is proposed, where the mixing structure is determined by a smooth transition tree architecture. Models based on mixture of distributions are useful in order to approximate unknown conditional distributions of multivariate data. The tree structure yields a model that is simpler, and in some cases more interpretable, than previous proposals in the literature. Based on the Expectation-Maximization (EM) algorithm a quasi-maximum likelihood estimator is derived and its asymptotic properties are derived under mild regularity conditions. In addition, a specific-to-general model building strategy is proposed in order to avoid possible identification problems. Both the estimation procedure and the model building strategy are evaluated in a Monte Carlo experiment, which give strong support for the theory developed in small samples. The approximation capabilities of the model is also analyzed in a simulation experiment. Finally, two applications with real datasets are considered. KEYWORDS: Mixture models, smooth transition, EM algorithm, asymptotic properties, time series, conditional distribution.
Year of publication: |
2007
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Authors: | Mendes, Eduardo F. ; Veiga, Alvaro ; Medeiros, Marcelo C. |
Publisher: |
Rio de Janeiro : Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia |
Saved in:
freely available
Series: | Texto para discussão ; 538 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 523038402 [GVK] hdl:10419/176021 [Handle] RePEc:rio:texdis:538 [RePEc] |
Source: |
Persistent link: https://www.econbiz.de/10011807362
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