Silvestro Di Sanzo () (Department of Economics, University Of Alicante)
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.
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Publisher Info
Paper provided by University of Venice "Ca' Foscari", Department of Economics in its series Working Papers with number
2007_03.
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