A hidden Markov approach to disability insurance

TL;DR

This paper introduces a hidden Markov model for estimating disability insurance rates, addressing limitations of traditional two-step methods by integrating stochastic process assumptions directly into the likelihood estimation.

Contribution

It proposes a novel approach that models the time trend as a stochastic process within a hidden Markov framework, improving estimation accuracy over traditional methods.

Findings

Model fits Swedish disability claims data effectively

Incorporates stochastic process into likelihood estimation

Enhances conceptual coherence of trend modeling

Abstract

Point and interval estimation of future disability inception and recovery rates are predominantly carried out by combining generalized linear models (GLM) with time series forecasting techniques into a two-step method involving parameter estimation from historical data and subsequent calibration of a time series model. This approach may in fact lead to both conceptual and numerical problems since any time trend components of the model are incoherently treated as both model parameters and realizations of a stochastic process. We suggest that this general two-step approach can be improved in the following way: First, we assume a stochastic process form for the time trend component. The corresponding transition densities are then incorporated into the likelihood, and the model parameters are estimated using the Expectation-Maximization algorithm. We illustrate the modelling procedure by fitting the model to Swedish disability claims data.