Robust identification of highly persistent interest rate regimes
| Autor: | Pietro Muliere, Antonietta Mira, Stefano Peluso |
|---|---|
| Přispěvatelé: | Peluso, S, Mira, A, Muliere, P |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Bayesian probability Markov process 02 engineering and technology Bayesian semiparametric State Space Model 01 natural sciences Dirichlet distribution Theoretical Computer Science 010104 statistics & probability symbols.namesake Interest rates 020901 industrial engineering & automation Bayesian nonparametric statistics Hidden Markov models Regime shifts State space model Software Artificial Intelligence Applied Mathematics Econometrics State space 0101 mathematics Mathematics Parametric statistics State-space representation Stochastic process Settore SECS-S/01 - STATISTICA symbols Probability distribution |
| Zdroj: | International Journal of Approximate Reasoning. 83:102-117 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2017.01.004 |
| Popis: | Parametric specifications in State Space Models (SSMs) are a source of bias in case of mismatch between modeling assumptions and reality. We propose a Bayesian semiparametric SSM that is robust to misspecified emission distributions. The Markovian nature of the latent stochastic process creates a temporal dependence and links the random probability distributions of the observations in a mixture of products of Dirichlet processes (MPDP). The model is shown to be adequate and it is applied to simulated data and to the motivating empirical problem of regime shifts in interest rates with latent state persistence. Bayesian semiparametric State Space model robust to imprecise emission distribution.Application: robustly identify highly persistent regime shifts in interest rates.Adequacy is shown to theoretically justify the model. |
| Databáze: | OpenAIRE |
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