A Simple Approach to Parameter Inference in State-Space Models

State-space models are popular tools in many areas of science, including economics, biology or ecology, and are mainly used to predict an unobserved (or latent) time series of interest from the available data. For instance, in economics we are often interested in inferring the risk of an asset from its price, while in ecology one may want to use some observations to estimate the evolution of the population size of a given group of animals.
As in any statistical models, state-space models depend on parameters that need to be learnt from the data. Parameter inference in this class of models is however known to be challenging, because computing the associated log-likelihood function, or its gradient, requires to integrate out the latent variables, a task which is usually intractable.
Particle filter algorithms have proven to be powerful Monte Carlo techniques for estimating expectations with respect to the unobserved variables, and state-of the art methods for parameter inference in state-space models amount to using these Monte Carlo estimates within "standard" algorithms (such as the Metropolis-Hastings algorithm for Bayesian inference, or the gradient ascent algorithm for maximum likelihood estimation). Despite the undeniable usefulness of these approaches, they are typically difficult to implement and to tune for non-experts in Monte Carlo methods, in addition to be computationally expensive even for moderate sample sizes. Consequently, in practice, simpler but theoretically unjustified algorithms are often preferred to learn the model parameter from the data.
The most popular of these simpler approaches is probably the one proposed 20 years ago by Liu and West (2001), in which the unknown model parameter is simply treated as an additional latent variable. If this strategy has the merit to be simple to implement it however has the drawback of not being supported by any theoretical results. Following the idea of Liu and West (2001), the objective of this research is to propose a theoretically justified way of treating the parameter of a state-space model as a latent variable in order to learn its value. To achieve this goal this research will build on the approach developed by Gerber and Douc (2021) for parameter estimation in static models, and leverage results on the concentration properties of the Bayesian posterior distributions that arise in partially observed Markov models (see Douc et al., 2020).
By providing an easy to implement but theoretically justified approach to parameter inference in the widely used class of state-space models, the research will benefit to a broad range of researchers and practitioners whose scientific conclusions and predictions rely on these models.

This project falls within the EPSRC Statistics and Applied Probability research area.