Quantile regression version of Hodrick–Prescott filter

Hiroshi Yamada ()
Additional contact information
Hiroshi Yamada: Hiroshima University

Empirical Economics, 2023, vol. 64, issue 4, No 5, 1645 pages

Abstract: Abstract Hodrick–Prescott (HP) filter is a popular trend filtering method for univariate macroeconomic time series such as real gross domestic product. This paper considers the quantile regression version of HP filter (qHP filter), which is a filtering method defined by replacing quadratic loss function of HP filter with quantile regression loss function. One of the essential properties of quantile regression is that if the regression includes intercept, then the ratio of negative residuals can be almost controlled. Does the suggested qHP filter also have the property? This paper answers this question. In addition to the main result, we provide an empirical illustration.

Keywords: Hodrick–Prescott filter; Quantile regression; Penalized regression (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00181-022-02292-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:empeco:v:64:y:2023:i:4:d:10.1007_s00181-022-02292-8

Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2

DOI: 10.1007/s00181-022-02292-8

Access Statistics for this article

Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund

More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().