Iterating marginalized Bayes maps for likelihood maximization with application to nonlinear panel models

TL;DR

This paper introduces a novel iterated filtering algorithm with marginalization for likelihood maximization, enabling efficient inference in high-dimensional nonlinear panel models that were previously computationally infeasible.

Contribution

The paper presents a new algorithm that incorporates marginalization into iterated filtering, improving likelihood inference for complex high-dimensional panel data models.

Findings

Enables likelihood-based inference for previously intractable models

Reduces computational issues in high-dimensional particle filtering

Broadens the scope of dynamic models applicable to panel data

Abstract

Complex dynamic systems can be investigated by fitting mechanistic stochastic dynamic models to time series data. In this context, commonly used Monte Carlo inference procedures for model selection and parameter estimation quickly become computationally unfeasible as the system dimension grows. The increasing prevalence of panel data, characterized by multiple related time series, therefore necessitates the development of inference algorithms that are effective for this class of high-dimensional mechanistic models. Nonlinear, non-Gaussian mechanistic models are routinely fitted to time series data but seldom to panel data, despite its widespread availability, suggesting that the practical difficulties for existing procedures are prohibitive. We investigate the use of iterated filtering algorithms for this purpose. We introduce a novel algorithm that contains a marginalization step that mitigates issues arising from particle filtering in high dimensions. Our approach enables likelihood-based inference for models that were previously considered intractable, thus broadening the scope of dynamic models available for panel data analysis.