Nonstationary nonlinear quantile regression

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This study examines estimation and inference based on quantile regression for parametric nonlinear models with an integrated time series covariate. We first derive the limiting distribution of the nonlinear quantile regression estimator and then consider testing for parameter restrictions, when the regression function is specified as an asymptotically homogeneous function. We also study linear-in-parameter regression models when the regression function is given by integrable regression functions as well as asymptotically homogeneous regression functions. We, furthermore, propose a fully modified estimator to reduce the bias in the original estimator under a certain set of conditions. Finally, simulation studies show that the estimators behave well, especially when the regression error term has a fat-tailed distribution.

Original languageEnglish
Pages (from-to)386-416
Number of pages31
JournalEconometric Reviews
Volume38
Issue number4
DOIs
Publication statusPublished - 2019 Apr 21
Externally publishedYes

Keywords

  • Integrated time series
  • nonlinear regression model
  • quantile regression

ASJC Scopus subject areas

  • Economics and Econometrics

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