NOTE: this is an early registration of the research idea and findings in form of slides for a talk presented at EAPS Mort workshop on 2021-09-22 (video: https://youtu.be/rOndHnuajH4?t=2370)Period Life Expectancy is the key summary measure of current mortality. Elimination of the direct influence of population age structure allows to meaningfully compare mortality levels and changes across the populations and over time. Calculation of life expectancy demands high quality detailed data on death and population counts disaggregated by sex and age. Such data is only available for the more developed countries. Moreover, even in the most developed countries, it becomes available with a considerable time lag. And for the majority of countries across the world timely and high quality deaths statistics is not available. In situations of mortality shocks such as the COVID–19 pandemic near real time mortality level comparisons are crucial.Building on the studied regularities of human mortality, we offer a method of reliable life expectancy short-casting based only on the time series of its previous values and the time series of total deaths counts observed in the population, not disaggregated by sex and age. The radical simplicity of the method allows to monitor changes in life expectancy in near real time, if time disaggregated (daily, weekly, or monthly) total death counts are available.
Non-Parametric Determination of Real-Time Lag Structure between Two Time Series: The 'Optimal Thermal Causal Path' Method