Preprint Article Version 1 This version is not peer-reviewed

General Quantile Time Series Regressions for Applications in Population Demographics

Version 1 : Received: 3 June 2018 / Approved: 12 June 2018 / Online: 12 June 2018 (15:05:09 CEST)

A peer-reviewed article of this Preprint also exists.

Peters, G.W. General Quantile Time Series Regressions for Applications in Population Demographics. Risks 2018, 6, 97. Peters, G.W. General Quantile Time Series Regressions for Applications in Population Demographics. Risks 2018, 6, 97.

Journal reference: Risks 2018, 6, 97
DOI: 10.3390/risks6030097

Abstract

This paper has three objectives, the first is to present a detailed overview in the form of a tutorial for the developments of several key quantile time series modelling approaches. The second objective is to develop a general framework to represent such quantile models in a unifying manner in order to easily develop extensions and connections between existing models that can then be developed to further extend these models in practice. In this regard, the core theme of the paper is to provide perspectives to a general audience of core components that go into construction of a quantile time series model and then to explore each of these core components in detail. The paper is not addressing the concerns of estimation of these models, as there is existing literature on these aspects in many settings, we provide references to relevant works on these aspects in several classes of model. Instead, the focus is rather to provide a unified framework to construct such models for practitioners, therefore the focus is instead on the properties of the models and links between such models from a constructive perspective. The third objective is to compare and discuss the application of the different quantile time series models on several sets of interesting demographic and mortality based time series data sets of relevance to life insurance analysis. The exploration included detailed mortality, fertility, births and morbidity data in several countries with more detailed analysis of regional data in England, Wales and Scotland.

Subject Areas

quantile regression; quantile time series; demographics; mortality; longevity; modelling mortality projection

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