Forecasting Time Series with Long Memory and Level Shifts, A Bayesian Approach

Silvestro Di Sanzo

No 2007_03, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: Recent studies have showed that it is troublesome, in practice, to distinguish between long memory and nonlinear processes. Therefore, it is of obvious interest to try to capture both features of long memory and non-linearity into a single time series model to be able to assess their relative importance. In this paper we put forward such a model, where we combine the features of long memory and Markov nonlinearity. A Markov Chain Monte Carlo algorithm is proposed to estimate the model and evaluate its forecasting performance using Bayesian predictive densities. The resulting forecasts are a significant improvement over those obtained by the linear long memory and Markov switching models.

Keywords: Markov-Switching models; Bootstrap; Gibbs Sampling (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
Pages: 35
Date: 2007
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ict and nep-ore
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2007_03

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