3:30pm - 5pm
Wednesday 24 March 2021

Econometrics Webinar: "Conditional Quantile Convergence: an Application to Growth-at-Risk"

Dr. Daniel Gutknecht (Goethe University Frankfurt) is presenting his latest work. Daniel's research interests lie within the fields of Theoretic and Applied (Micro-) Econometrics, Labor and Health Economics.

Free

University of Surrey
Guildford
Surrey
GU2 7XH
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Meeting ID: 913 8583 6374

Title: Conditional Quantile Convergence: an Application to Growth-at-Risk

Abstract

This paper proposes tests for pairwise and multiple out-of-sample comparisons of parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage w.r.t. the \textit{union} of information sets across models, for a given loss function. Our approach operates over a weighted range of quantile ranks, thereby assessing models’ relative forecast ability across different quantile levels simultaneously. We derive the limiting distribution and establish the first order validity of block bootstrap critical values. An empirical application to Growth-at-Risk (GaR) uncovers situations where a threshold quantile model improves over the standard linear quantile regression approach.

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