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mortalityssm

State-space models for statistical mortality projections

https://github.com/rokasgy/mortalityssm

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Links to: mdpi.com
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    Scientific vocabulary similarity
    Low similarity (6.9%) to scientific vocabulary

Keywords

mortality-forecasting particle-filter r state-space-model value-at-risk
Last synced: 9 months ago · JSON representation ·

Repository

State-space models for statistical mortality projections

Basic Info
  • Host: GitHub
  • Owner: RokasGy
  • License: gpl-3.0
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 33.2 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
mortality-forecasting particle-filter r state-space-model value-at-risk
Created almost 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

mortalitySSM

State-space models for statistical mortality projections. The code was developed to perform stachastic Lee-Carter model mortality projections using state-space model set up. Two separate models are provided: - basic linear model (DLM -Dynamic Linear Model); - DLM with regime switching between low and high volatility regimes. Parameter fitting is performed using MCMC Gibbs sampler. The model useses R dlm package to perform Kalman filtering. As model application, the code is provided for calculation of mortality VAR (Value-at-Risk).

As the imput the code uses mortality data obtained from Human Mortality Database https://mortality.org/.

The following files are uploaded: - Lee-Carter DLM with switching 2022 03.R The code was used to derive mortality projections for the Swedish population in the article: https://www.mdpi.com/2227-7390/8/7/1053. - Lee-Carter DLM with switching v2 2022 07.R Updated version of the model, which also includes stochastic modelling of alpha(x) parameters. - Mortality risk VAR model.R Model used the calculate VAR rates. See article https://www.mdpi.com/2227-9091/7/2/58 for the description of the underlying methodology. - Particle filter for likelihood estimation.R The code used to estimate log-likelihood conditional on estimated parameters. See article: https://www.mdpi.com/2227-7390/8/7/1053 for the description of the methodology.

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  State-space Lee-Carter mortality projection
  software
message: >-
  If you use this software, please cite both the
  related article (as indicated in README) and the
  software itself.
type: software
authors:
  - given-names: Rokas
    family-names: Gylys

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