A closed-form filter for binary time series
| Autor: | Sonia Petrone, Giovanni Rebaudo, Daniele Durante, Augusto Fasano |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences PARTICLE FILTER Computer science Gaussian Monte Carlo method Inference Multivariate normal distribution DYNAMIC PROBIT MODEL 01 natural sciences Theoretical Computer Science Methodology (stat.ME) 010104 statistics & probability symbols.namesake STATE-SPACE MODEL 0502 economics and business Dynamic probit model Kalman filter Particle filter State-space model sun 0101 mathematics Statistics - Methodology 050208 finance DYNAMIC PROBIT MODEL KALMAN FILTER PARTICLE FILTER STATE-SPACE MODEL SUN Cumulative distribution function 05 social sciences SUN Computational Theory and Mathematics Binary data symbols Statistics Probability and Uncertainty KALMAN FILTER Algorithm Smoothing |
| Popis: | Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models — where filtering, predictive and smoothing distributions are available in closed form — binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the Gaussian states and the likelihood induced by the observation equation for the binary data. In this article we prove that the filtering, predictive and smoothing distributions in dynamic probit models with Gaussian state variables are, in fact, available and belong to a class of unified skew-normals (sun) whose parameters can be updated recursively in time via analytical expressions. Also the key functionals of these distributions are, in principle, available, but their calculation requires the evaluation of multivariate Gaussian cumulative distribution functions. Leveraging sun properties, we address this issue via novel Monte Carlo methods based on independent samples from the smoothing distribution, that can easily be adapted to the filtering and predictive case, thus improving state-of-the-art approximate and sequential Monte Carlo inference in small-to-moderate dimensional studies. Novel sequential Monte Carlo procedures that exploit the sun properties are also developed to deal with online inference in high dimensions. Performance gains over competitors are outlined in a financial application. |
| Databáze: | OpenAIRE |
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