2015
DOI: 10.1002/for.2353
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A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates

Abstract: Observing that a sequence of negative logarithms of 1‐year survival probabilities displays a linear relationship with the sequence of corresponding terms with a time lag of a certain number of years, we propose a simple linear regression to model and forecast mortality rates. Our model assuming the linearity between two mortality sequences with a time lag each other does not need to formulate the time trends of mortality rates across ages for mortality prediction. Moreover, the parameters of our model for a gi… Show more

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Cited by 9 publications
(5 citation statements)
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“…A linear relationship between two sequences of mortality rates has been used in mortality modeling (see Lin & Tsai, 2015; Lin et al, 2015; Tsai & Yang, 2015) and in natural hedges with mortality immunization (see Tsai & Chung, 2013; Lin & Tsai, 2013, 2014). The force of mortality–interest moves approximately linearly, which also has been used in Lin and Tsai (2020) for natural hedges with mortality–interest immunization.…”
Section: Modeling Mortality–interest Ratesmentioning
confidence: 99%
“…A linear relationship between two sequences of mortality rates has been used in mortality modeling (see Lin & Tsai, 2015; Lin et al, 2015; Tsai & Yang, 2015) and in natural hedges with mortality immunization (see Tsai & Chung, 2013; Lin & Tsai, 2013, 2014). The force of mortality–interest moves approximately linearly, which also has been used in Lin and Tsai (2020) for natural hedges with mortality–interest immunization.…”
Section: Modeling Mortality–interest Ratesmentioning
confidence: 99%
“…Moreover, the linear regression was chosen for forecasting because of rapid price changes which can be observed in the analyzed time series. Moreover, this model is widely used in the scientific research, for example by Lin, Tsai (2015). For calculating linear regression forecasts the model from the scikit-learn library was used.…”
Section: Linear Regressionmentioning
confidence: 99%
“…According to whether the mapping relationship between the input space and the output space is established before prediction, the data-driven prediction methods can be divided into eager prediction methods and lazy prediction methods. The eager prediction methods mainly include various regression algorithms in statistics [5], artificial neural networks (ANN) [6][7], support vector machine regression (SVMR) models [8][9] in machine learning, grey forecast models in grey theory [10][11], etc. The lazy prediction methods mainly include k-nearest neighbor algorithms (k-NN) [12], locally weighted regressions (LWR) [13][14], etc.…”
Section: Introductionmentioning
confidence: 99%