Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective

Cristina Davino (), Pasquale Dolce (), Stefania Taralli () and Domenico Vistocco
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Cristina Davino: University of Naples Federico II
Pasquale Dolce: University of Naples Federico II
Stefania Taralli: ISTAT

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2022, vol. 161, issue 2, No 27, 907-936

Abstract: Abstract Quantile composite-based path modeling is a recent extension to the conventional partial least squares path modeling. It estimates the effects that predictors exert on the whole conditional distributions of the outcomes involved in path models and provides a comprehensive view on the structure of the relationships among the variables. This method can also be used in a predictive way as it estimates model parameters for each quantile of interest and provides conditional quantile predictions for the manifest variables of the outcome blocks. Quantile composite-based path modeling is shown in action on real data concerning well-being indicators. Health outcomes are assessed taking into account the effects of Economic well-being and Education. In fact, to support an accurate evaluation of the regional performances, the conditions within the outcomes arise should be properly considered. Assessing health inequalities in this multidimensional perspective can highlight the unobserved heterogeneity and contribute to advances in knowledge about the dynamics producing the well-being outcomes at local level.

Keywords: PLS path modeling; Quantile composite-based path modeling; Conditional quantile prediction; Well-being; Territorial inequalities; Health indicators (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11205-020-02425-5

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