Logistic mixtures vs. Markov chain Monte Carlo
Economic time series models and innovations have undergone through tremendous changes over the years, thus providing answers to some of the unsolvable problems. With regime switching time series models, simulated data is used to compare the unobserved state vector if it follows a Markov Chain Monte...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2008
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/5070 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Economic time series models and innovations have undergone through tremendous changes over the years, thus providing answers to some of the unsolvable problems. With regime switching time series models, simulated data is used to compare the unobserved state vector if it follows a Markov Chain Monte Carlo or a Logistic Mixture Process. By using OpenBUGS, results for Markov Chain Monte Carlo are obtained. Also, by using Statistica and EVIEWS, results for Logistic Mixture Model are obtained. AR(1) is then applied to the real data to determine the existence of regimes.;"This paper is based on the comparison of regime switching models by Paliouras (2007) entitled Comparing Regime-Switching Models in Time Series: Logistic Mixtures vs. Markov switching. " |
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