183 research outputs found

    Modeling Long-Term Electricity Forward Prices

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    Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals

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    BACKGROUND: The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire the diagnostic accuracy of length of sick leaves predicted in the Norwegian National Insurance Offices, and to compare their predictions with the self-predictions of the sick-listed. METHODS: Based on medical certificates, two National Insurance medical consultants and two National Insurance officers predicted, at day 14, the length of sick leave in 993 consecutive cases of sick leave, resulting from musculoskeletal or mental disorders, in this 1-year follow-up study. Two months later they reassessed 322 cases based on extended medical certificates. Self-predictions were obtained in 152 sick-listed subjects when their sick leave passed 14 days. Diagnostic accuracy of the predictions was analysed by ROC area, sensitivity, specificity, likelihood ratio, and positive predictive value was included in the analyses of predictive validity. RESULTS: The sick-listed identified sick leave lasting 12 weeks or longer with an ROC area of 80.9% (95% CI 73.7–86.8), while the corresponding estimates for medical consultants and officers had ROC areas of 55.6% (95% CI 45.6–65.6%) and 56.0% (95% CI 46.6–65.4%), respectively. The predictions of sick-listed males were significantly better than those of female subjects, and older subjects predicted somewhat better than younger subjects. Neither formal medical competence, nor additional medical information, noticeably improved the diagnostic accuracy based on medical certificates. CONCLUSION: This study demonstrates that the accuracy of a prognosis based on medical documentation in sickness absence forms, is lower than that of one based on direct communication with the sick-listed themselves

    Electricity Futures Prices

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    This paper provides insight in the time-varying relation between electricity futures prices and fundamentals in the form of prices of contracts for fossil fuels. As supply curves are not constant and different producers have different marginal costs of production, we argue that the relation between electricity futures prices and futures prices of underlying fundamentals such as natural gas, coal and emission rights are not constant and vary over time. We test this view by applying a model that linearly relates electricity futures prices to the marginal costs of production and calculate the log-likelihood of different time-varying and constant specifications of the coefficients. To do so, we formulate the model in state-space form and apply the Kalman Filter to observe the dynamics of the coefficients. We analyse historical prices of futures contracts with different delivery periods (calendar year and seasons, peak and off-peak) from Germany and the U.K. The results indicate that analysts should choose a time-varying specification to relate the futures price of power to prices of underlying fundamentals

    Reliability of sickness certificates in detecting potential sick leave reduction by modifying working conditions: a clinical epidemiology study

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    BACKGROUND: Medical sickness certificates are generally the main source for information when scrutinizing the need for aimed intervention strategies to avoid or reduce the individual and community side effects of sick leave. This study explored the value of medical sickness certificates related to daily work in Norwegian National Insurance Offices to identify sick-listed persons, where modified working conditions might reduce the ongoing sick leave. METHODS: The potential for reducing the ongoing sick leave by modifying working conditions was individually assessed on routine sickness certificates in 999 consecutive sick leave episodes by four Norwegian National Insurance collaborators, two with and two without formal medical competence. The study took place in Northern Norway in 1997 and 1998. Agreement analysed with differences against mean, kappa, and proportional-agreement analysis within and between groups of assessors was used in the judgement. Agreements between the assessors and the self-assessment of sick-listed subjects were additionally analysed in 159 sick-leave episodes. RESULTS: Both sick-listed subjects and National Insurance collaborators anticipated a potential reduction in sick leave in 20–30% of cases, and in another 20% the potential was assessed as possible. The chance corrected agreements, however, were poor (k < 0.20) within and between groups of National Insurance collaborators. The agreement between National Insurance collaborators and the sick-listed subjects was no better than chance. Neither extended medical information nor formal medical competence increased agreement in cases where modified working conditions might have reduced sick leave. CONCLUSION: Information in medical sickness certificates proved ineffective in detecting cases where modified working conditions may reduce sick leave, and focusing on medical certificates may prevent identification of needed interventions. Strategies on how to communicate directly with sick-listed subjects would enable social authorities to exploit more of the sick leave reduction potential by modifying the working conditions than strategies on improving medical information

    A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection

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    We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve problems that can not be handled by any of known polynomial optimization solvers.
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