A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory

Nima Nonejad ()
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Nima Nonejad: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications.

Keywords: Mixture innovation models; Markov chain Monte Carlo; Realized volatility (search for similar items in EconPapers)
JEL-codes: C11 C22 C51 C53 (search for similar items in EconPapers)
Pages: 24
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2013-24

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