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Season's Greetings to all our readers!
\[y = \frac{\log_e\left(\frac{x}{m}-sa\right)}{r^2}\]
\[\Rightarrow yr^2 = \log_e\left(\frac{x}{m}-sa\right)\]
\[\Rightarrow e^{yr^2} = \frac{x}{m}-sa\]
\[\Rightarrow me^{yr^2} = x-msa\]
\[\Rightarrow me^{rry} = x-mas\]
Signal or noise?
Each year since 2009 the CMI in the UK has released a spreadsheet tool for actuaries to use for mortality projections. I have written about this tool a number of times, including how one might go about setting the long-term rate. The CMI now wants to change how the spreadsheet is calibrated and has proposed the following model in CMI (2016a):
\[\log m_{x,y} = \alpha_x + \beta_x(y-\bar y) + \kappa_y + \gamma_{y-x}\qquad (1)\]
Making sense of senescence
Getting to the root of time-series forecasting
When using a stochastic model for mortality forecasting, people can either use penalty functions or time-series methods . Each approach has its pros and cons, but time-series methods are the commonest. I demonstrated in an earlier posting how an ARIMA time-series model can be a better representation of a mortality index than a random walk with drift.