TY - JOUR
T1 - Locally Stationary Quantile Regression for Inflation and Interest Rates
AU - Xu, Zhuying
AU - Kim, Seonjin
AU - Zhao, Zhibiao
N1 - Funding Information:
We are grateful to an associate editor and two referees for their constructive comments. Part of this work is based on Zhuying Xu’s PhD dissertation at Penn State University.
Publisher Copyright:
© 2021 American Statistical Association.
PY - 2022
Y1 - 2022
N2 - Motivated by the potential time-varying and quantile-specific relation between inflation and interest rates, we propose a locally stationary quantile regression approach to model the inflation and interest rates relation. Large sample theory for estimation and inference of quantile-varying and time-varying coefficients are established. In empirical analysis of inflation and interest rates relation, it is found that the estimated functional coefficients vary with time in a complicated manner. Furthermore, the relation is quantile-specific: not only do the selected orders differ for different quantiles, but also the coefficients corresponding to different quantiles can display completely different patterns.
AB - Motivated by the potential time-varying and quantile-specific relation between inflation and interest rates, we propose a locally stationary quantile regression approach to model the inflation and interest rates relation. Large sample theory for estimation and inference of quantile-varying and time-varying coefficients are established. In empirical analysis of inflation and interest rates relation, it is found that the estimated functional coefficients vary with time in a complicated manner. Furthermore, the relation is quantile-specific: not only do the selected orders differ for different quantiles, but also the coefficients corresponding to different quantiles can display completely different patterns.
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U2 - 10.1080/07350015.2021.1874389
DO - 10.1080/07350015.2021.1874389
M3 - Article
AN - SCOPUS:85100959641
SN - 0735-0015
VL - 40
SP - 838
EP - 851
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
IS - 2
ER -