Bayesian Nonparametric State Space Models via Mixture Process of Products of Dirichlet Processes

Stefano Peluso, Antonietta Mira, Pietro Muliere

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

Parametrically specified measurement and transition equations in State Space Models (SSM) are a source of bias in case of a mismatch between parametric assumptions and reality. The mixture process of products of Dirichlet processes (MPDP) is proposed as a flexible modeling framework for SSMs when there is uncertainty on the distributional assumption in the measurement equation. It is shown that the MPDP prior can approximate any prior belief and that the true parametric SSM can be approximated arbitrarily well by a nonparametric SSM with MPDP prior on the conditional distribution of the observations. An efficient estimation algorithm is designed for posterior sampling, with minimum additional computational effort relative to parametric models. Two simulated exercises on Gaussian Kalman Filtering and Hidden Markov Models, and an empirical application for regime shifts in interest rates, show the better performance of the proposed approach when compared to parametric SSMs.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of abstracts of the 8th International Conference on Computational and Financial Econometrics and the 7th International Conference of the European Research Consortium for Informatics and Mathematics
Pagine1
Numero di pagine1
Stato di pubblicazionePubblicato - 2014
Evento8th International Conference on Computational and Financial Econometrics and the 7th International Conference of the European Research Consortium for Informatics and Mathematics - PISA -- ITA
Durata: 6 dic 20148 dic 2014

Convegno

Convegno8th International Conference on Computational and Financial Econometrics and the 7th International Conference of the European Research Consortium for Informatics and Mathematics
CittàPISA -- ITA
Periodo6/12/148/12/14

Keywords

  • Mixture of Dirichlet Processes
  • State Space Models

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