# A closed-form filter for binary time series

## TL;DR

This paper introduces a closed-form filtering approach for binary time series within Gaussian state-space models, utilizing the skew-normal distribution to enable exact inference and improve computational efficiency.

## Contribution

It proves that filtering, predictive, and smoothing distributions in dynamic probit models are analytically tractable using the SUN distribution, with recursive parameter updates.

## Key findings

- Exact filtering distributions are derived for binary time series.
- Novel Monte Carlo methods based on SUN properties improve inference accuracy.
- Performance surpasses existing methods in a financial application.

## Abstract

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.