Activity Number:
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168
- Bayesian Models for Gaussian and Point Processes
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #323920
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View Presentation
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Title:
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A Markov Switching Causal-Noncausal Autoregressive Model with Application to Economic Bubbles
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Author(s):
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Anton Lobach* and Gavino Puggioni
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Companies:
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University of Rhode Island. Dept. of Computer Science and Statistics and University of Rhode Island, Department of Computer Science and Statistics
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Keywords:
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Mixture models ;
Heavy tails errors ;
Forecasting ;
Bayesian models ;
Exchange rates ;
Noncausal processes
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Abstract:
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Economic bubble phenomena may result in explosive trends followed by a steep decline in financial time series. A bubble can be viewed as a positive feedback loop, when investors' future profits expectations influence the present market value of securities and vice versa. These dynamics can be better captured by mixed causal-noncausal autoregressive processes in comparison to traditional ARIMA models. In this work we propose Markov switching mixed causal-noncausal autoregressive processes (MSMAR) to account for changes in regime at different times. Parameter estimation is performed in a Bayesian framework via MCMC algorithms. The model is tested for performance with a simulation study and then applied to Bitcoin/USD exchange rate and US inflation data.
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Authors who are presenting talks have a * after their name.