 Sequential quantile regression for stream data by least squaresYe Fan and Nan Lin Journal of Econometrics, 2025, vol. 249, issue PA Abstract: Massive stream data are common in modern economics applications, such as e-commerce and finance. They cannot be permanently stored due to storage limitation, and real-time analysis needs to be updated frequently as new data become available. In this paper, we develop a sequential algorithm, SQR, to support efficient quantile regression (QR) analysis for stream data. Due to the non-smoothness of the check loss, popular gradient-based methods do not directly apply. Our proposed algorithm, partly motivated by the Bayesian QR, converts the non-smooth optimization into a least squares problem and is hence significantly faster than existing algorithms that all require solving a linear programming problem in local processing. We further extend the SQR algorithm to composite quantile regression (CQR), and prove that the SQR estimator is unbiased, asymptotically normal and enjoys a linear convergence rate under mild conditions. We also demonstrate the estimation and inferential performance of SQR through simulation experiments and a real data example on a US used car price data set. Keywords: Bayesian inference; Least squares; Quantile regression; Sequential algorithm; Stream data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:249:y:2025:i:pa:s0304407624001374 DOI: 10.1016/j.jeconom.2024.105791 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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