EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH

In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed...

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Vydáno v:ASTIN Bulletin : The Journal of the IAA Ročník 52; číslo 3; s. 789 - 812
Hlavní autoři: Miyata, Akihiro, Matsuyama, Naoki
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.09.2022
Témata:
Accuracy
Actuarial science
Age
Approximation
Confidence intervals
Mortality
Neural networks
Neurons
Time series
state-space model
Lee–Carter model
variational Bayesian inference
variational autoencoder
ISSN:0515-0361, 1783-1350
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Shrnutí:In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0515-0361
1783-1350
DOI:10.1017/asb.2022.15