A Quantile Regression Model for Time-Series Data in the Presence of Additive Components

Yebin Cheng and Dawit Zerom ()

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 20, 4354-4379

Abstract: In this article, we propose a kernel-based estimator for the finite-dimensional parameter of a partially additive linear quantile regression model. For dependent processes that are strictly stationary and absolutely regular, we establish a precise convergent rate and show that the estimator is root-n consistent and asymptotically normal. To help facilitate inferential procedures, a consistent estimator for the asymptotic variance is also provided. In addition to conducting a simulation experiment to evaluate the finite sample performance of the estimator, an application to US inflation is presented. We use the real-data example to motivate how partially additive linear quantile models can offer an alternative modeling option for time-series data.

Date: 2015
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DOI: 10.1080/03610926.2013.844839

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