A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters

TL;DR

This paper introduces a novel Square-Root Second-Order Extended Kalman Filtering method for accurately estimating smoothly changing parameters in dynamic models, useful for analyzing complex psychological and behavioral processes over time.

Contribution

The paper presents a new filtering approach capable of estimating time-varying parameters in dynamic factor models, addressing challenges in modeling complex, evolving psychological data.

Findings

Accurately recovers unobserved states in simulated dynamic models.

Effectively characterizes time-varying effects of interventions.

Demonstrates utility in real-world emotional data analysis.

Abstract

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.