A Hidden markov approach to disability insurance

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<title>Hidden markov approach to disability insurance</title>
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<namePart>Löfdahl, Björn</namePart>
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<abstract displayLabel="Summary">Point and interval estimation of future disability inception and recovery rates is predominantly carried out by combining generalized linear models 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 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 modeling procedure by fitting the model to Swedish disability claims data</abstract>
<note type="statement of responsibility">Boualem Djehiche, Björn Löfdahl</note>
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<topic>Modelo de Markov</topic>
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<topic>Discapacidad</topic>
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<topic>Seguro de incapacidad</topic>
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<topic>Procesos estocásticos</topic>
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<topic>Matemática del seguro</topic>
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<topic>Modelos estadísticos</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>05/03/2018 Tomo 22 Número 1 - 2018 , p. 119-136</text>
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