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Related Concept Videos

Life Tables01:22

Life Tables

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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Hazard Rate01:11

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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Subjective mortality risk and bequests.

Li Gan1,2, Guan Gong3, Michael Hurd4

  • 1Southwestern University of Finance and Economics, China.

Journal of Econometrics
|October 26, 2019
PubMed
Summary
This summary is machine-generated.

Subjective expectations about life expectancy significantly improve predictions of later-life wealth. Using these personal survival beliefs in economic models better explains consumption and savings, suggesting most bequests are unintentional.

Keywords:
BequestC81D91Life-cycle modelMedian regressionSubjective mortality risk

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Area of Science:

  • Economics
  • Gerontology
  • Behavioral Finance

Background:

  • Understanding wealth accumulation and decumulation in later life is crucial for financial planning and policy.
  • Traditional economic models often rely on objective life-table data, which may not fully capture individual financial decision-making.

Purpose of the Study:

  • To investigate whether subjective expectations of life expectancy predict wealth holding patterns in older adults.
  • To compare the predictive power of subjective survival rates versus objective life-table rates within a life-cycle model.

Main Methods:

  • Estimation of a structural life-cycle model with bequests using panel data from the Asset and Health Dynamics among the Oldest Old (AHEAD) study.
  • Utilizing a Bayesian updating method to estimate individuals' subjective survival rates based on their beliefs about survival probabilities.
  • Comparing model performance in predicting wealth holdings using subjective versus objective survival data.

Main Results:

  • The life-cycle model incorporating subjective survival rates demonstrated superior performance in predicting wealth holdings compared to models using life-table survival rates.
  • Subjective survival expectations were found to be a significant determinant of consumption and savings decisions among older adults.
  • Analysis indicated that a substantial portion of bequests are involuntary or accidental, rather than planned.

Conclusions:

  • Subjective expectations regarding life expectancy are vital for accurately modeling financial behavior in later life.
  • Incorporating individual-level survival beliefs into economic models enhances their predictive accuracy for wealth dynamics.
  • The findings suggest a need to reconsider the role of intentionality in bequest behavior, highlighting the prevalence of unplanned inheritances.