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A Unifying Switching Regime Regression Framework with Applications in Health Economics

Marra, G., Radice, R. ORCID: 0000-0002-6316-3961 & Zimmer, D. (2023). A Unifying Switching Regime Regression Framework with Applications in Health Economics. Econometric Reviews, doi: 10.1080/07474938.2023.2255438


Motivated by three health economics-related case studies, we propose a unifying and flexible regression modelling framework that involves regime switching. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the dependence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalised maximum likelihood estimation approach. The proposed modelling framework is employed in three applications in health economics, that use data from the Medical Expenditure Panel Survey, where novel patterns are uncovered. The framework has been incorporated in the R package GJRM, hence allowing users to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries

Publication Type: Article
Additional Information: © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this articlehas been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Publisher Keywords: copula; penalised regression spline; simultaneous estimation; structural equation model, switching regime
Subjects: H Social Sciences > HB Economic Theory
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments: Bayes Business School > Actuarial Science & Insurance
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