Forecasting Time Series with Long Memory and Level Shifts, A Bayesian Approach
AbstractRecent 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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Bibliographic InfoPaper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2007_03.
Date of creation: 2007
Date of revision:
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More information through EDIRC
Markov-Switching models; Bootstrap; Gibbs Sampling;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-02-09 (All new papers)
- NEP-ECM-2008-02-09 (Econometrics)
- NEP-ETS-2008-02-09 (Econometric Time Series)
- NEP-FOR-2008-02-09 (Forecasting)
- NEP-ICT-2008-02-09 (Information & Communication Technologies)
- NEP-ORE-2008-02-09 (Operations Research)
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