Panel data analysis via mechanistic models

TL;DR

This paper introduces a flexible likelihood-based inference framework for nonlinear, partially observed panel data models using iterated filtering, demonstrated on epidemiological case studies.

Contribution

It develops a novel inference methodology for arbitrary nonlinear panel models that leverages simulation-based likelihood estimation, applicable to complex dynamic systems.

Findings

Successfully applied to epidemiological data sets

Addresses computational challenges in large datasets

Provides a flexible framework for nonlinear panel model inference

Abstract

Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size.