pystmomo 0.1.1

Stochastic mortality modelling — Python port of the StMoMo R library

pyStMoMo

Stochastic Mortality Modelling in Python — a faithful Python port of the StMoMo R library by Villegas, Millossovich & Kaishev.

Overview

pyStMoMo implements a framework for fitting, forecasting, simulating and validating Generalised Age-Period-Cohort (GAPC) stochastic mortality models, including:

Model	Reference
Lee-Carter (LC)	Lee & Carter (1992)
Cairns-Blake-Dowd (CBD)	Cairns et al. (2006)
Age-Period-Cohort (APC)	Currie (2006)
Renshaw-Haberman (RH)	Renshaw & Haberman (2006)
M6, M7, M8	Cairns et al. (2009)
Custom GAPC	—

Quick Start

import pystmomo as ps

data = ps.load_ew_male()
fit  = ps.lc().fit(data.deaths, data.exposures, ages=data.ages, years=data.years)
fc   = ps.forecast(fit, h=50)
sim  = ps.simulate(fit, nsim=5000, h=50, seed=42)

ps.plot_parameters(fit)
ps.plot_fan(sim, age=65)

Installation

pip install pystmomo

From source

git clone https://github.com/filipeclduarte/pyStMoMo
cd pyStMoMo
pip install -e ".[dev]"

Documentation

Full documentation at https://filipeclduarte.github.io/pyStMoMo.

References

• Villegas, A.M., Millossovich, P., & Kaishev, V.K. (2018). StMoMo: An R Package for Stochastic Mortality Modelling. Journal of Statistical Software, 84(3).
• Lee, R.D., & Carter, L.R. (1992). Modeling and Forecasting U.S. Mortality. JASA, 87(419), 659–671.
• Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., & Balevich, I. (2009). A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States. NAAJ, 13(1), 1–35.

License

GPL-2.0-or-later — see LICENSE.