Estimating the demand for health care with panel data: a semiparametric Bayesian approach

Jochmann, Markus; Leon-Gonzalez, Roberto

doi:10.1002/hec.936

Cited by 28 publications

(25 citation statements)

References 35 publications

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“…Examples for Bayesian simulations are, Kaufmann (2000), Tsionas (2000), Jacobson and Karlsson (2004) and Jochmann and Leon-Gonzalez (2004). Tsionas (2000) uses stock market data to check different models about the stochastic movements of prices.…”

Section: Discussion and Classification Of Simulation Modelsmentioning

confidence: 99%

“…A similar approach is taken by Kaufmann (2000). Bayesian simulations, however, can also be used to make predictions about future developments (see, e.g., Jacobson andKarlsson, 2004 andJochmann andLeon-Gonzalez 2004). Jacobson and Karlsson (2004) use the Bayesian approach to evaluate the relevance of certain potential indicators in forecasting inflation in Sweden.…”

Section: Discussion and Classification Of Simulation Modelsmentioning

confidence: 99%

“…This resulting knowledge can then be used to make predictions. Jochmann and Leon-Gonzalez (2004) used the Bayesian principle to estimate the parameters and features of a model which predicts the demand for health care by classifying the population into classes according to their health status.…”

Section: Discussion and Classification Of Simulation Modelsmentioning

confidence: 99%

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A Taxonomy of Inference in Simulation Models

Brenner¹,

Werker²

2007

Comput Econ

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Simulation models have become increasingly popular in economics in the last two decades, because they can deal with a wide range of research questions. The set-up and analysis of simulation models can range from very specific to very general and can be underpinned by different combinations of theoretical considerations and empirical data. We offer a taxonomy of existing simulation approaches and show how their results can be used to explain observed economic features, examine economic systems and predict future economic processes. Moreover, we offer a new type of method that helps to better exploit empirical findings in simulation models.

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Section: Discussion and Classification Of Simulation Modelsmentioning

confidence: 99%

Section: Discussion and Classification Of Simulation Modelsmentioning

confidence: 99%

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A Taxonomy of Inference in Simulation Models

Brenner¹,

Werker²

2007

Comput Econ

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“…Thus, in order to protect the derived inferences against potential misspecification effects, we opt for a semiparametric approach based on a Dirichlet Process prior. A similar approach to modelling random effects, but with a single outcome and without splines, has been proposed in the existing literature (Jochmann and Leon-Gonzalez 2004).…”

Section: Introductionmentioning

confidence: 99%

Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model

2011

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Monograph:Mukherji, A., Roychowdhury, S., Ghosh, P. et al. ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. In this paper, we analyse two frequently used measures of the demand for health care, namely hospital visits and out-of-pocket health care expenditure, which have been analysed separately in the existing literature. Given that these two measures of healthcare demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure. Furthermore, the joint framework allows for the presence of non-linear effects of covariates using splines to capture the effects of aging on healthcare demand. Sample heterogeneity is modelled robustly with the random effects following Dirichlet process priors with explicit cross-part correlation. The findings of our empirical analysis of the U.S. Health and Retirement Survey indicate that the demand for healthcare varies with age and gender and exhibits significant cross-part correlation that provides a rich understanding of how aging affects health care demand, which is of particular policy relevance in the context of an aging population. Sheffield Economic Research Paper

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“…There, the frequency distribution of doctor consultations is a primary indicator of health care utilization in a population and, as such, is of obvious importance for health care budgeting. Therefore, patterns in the frequency of consultations to the doctor, especially the dependency of the utilization of health resources on demographic, socioeconomic and geographic factors has been extensively documented, and its proper modeling is of central interest for empirical research in health economics and applied econometrics, respectively (Cameron et al, 1988;Pohlmeier and Ulrich, 1995;Windmeijer and Santos Silva, 1997;Deb and Trivedi, 1997;Jochmann and León-González, 2004;Winkelmann, 2004; to mention only few). Typically, the literature bemoans a lack of data on certain key variables on the one hand and the 'shrinkage effect' effect of conditional expectations on the other hand.…”

Section: Introductionmentioning

confidence: 99%

Estimating and Predicting the Distribution of the Number of Visits to the Medical Doctor

Dai

Zucchini²,

Sperlich

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn many countries the demand for health care services is of increasing importance. Especially in the industrialized world with a changing demographic structure social insurances and politics face real challenges. Reliable predictors of those demand functions will therefore become invaluable tools. This article proposes a prediction method for the distribution of the number of visits to the medical doctor for a determined population, given a sample that is not necessarily taken from that population. It uses the estimated conditional sample distribution, and it can be applied for forecast scenarios. The methods are illustrated along data from Sidney. The introduced methodology can be applied as well to any other prediction problem of discrete distributions in real, future or any fictitious population. It is therefore also an excellent tool for future predictions, scenarios and policy evaluation. 1 keywords: predicting health care demand, visits to the doctor, health economics, model selection JEL classification: I12, C51, C53, H75.

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Estimating the demand for health care with panel data: a semiparametric Bayesian approach

Cited by 28 publications

References 35 publications

A Taxonomy of Inference in Simulation Models

A Taxonomy of Inference in Simulation Models

Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model

Estimating and Predicting the Distribution of the Number of Visits to the Medical Doctor

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