Artikli pealkiri: Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models

Autorid: 
David Kohns - Aalto University
Tibor Szendrei - National Institute of Economic and Social Research

Lühikokkuvõte:

Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regression functions toward the desired non-crossing space. We achieve this by estimating multiple quantiles jointly with a prior on variation across quantiles, a fused shrinkage prior with quantile adaptivity. The posterior is derived from a decision-theoretic general Bayes perspective, whose form yields a natural state-space interpretation aligned with Time-Varying Parameter (TVP) models. Taken together our approach leads to a Quantile-Varying Parameter (QVP) model, for which we develop efficient sampling algorithms. We demonstrate that our proposed modelling framework provides superior parameter recovery and predictive performance compared to competing Bayesian and frequentist quantile regression estimators in simulated experiments and a real-data application to multivariate quantile estimation in macroeconomics.

Tallinna Tehnikaülikooli majandusanalüüsi ja rahanduse instituudi avatud teadusseminarid toimuvad tavaliselt kuu teisel ja neljandal kolmapäeval nii kohapealse kui ka veebis osalemisvõimalusega, kui ei ole teatatud teisiti. Ettekanne kestab u 45 minutit, millele järgneb veerand tundi arutelu. Seminar toimub inglise keeles. Ettekande aluseks olev artikkel on üldjuhul kättesaadav seminaril kohapeal. Küsimuste korral võib pöörduda seminaride koordinaatori Karsten Staehri karsten.staehr@taltech.ee poole.