Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression
Econometrics, 2017, vol. 5, issue 1, 1-11
Filters constructed on the basis of standard local polynomial regression (LPR) methods have been used in the literature to estimate the business cycle. We provide a frequency domain interpretation of the contrast filter obtained by the difference of a series and its long-run LPR component and show that it operates as a kind of high-pass filter, so that it provides a noisy estimate of the cycle. We alternatively propose band-pass local polynomial regression methods aimed at isolating the cyclical component. Results are compared to standard high-pass and band-pass filters. Procedures are illustrated using the US GDP series.
Keywords: business cycles; local polynomial regression; filtering; high-pass; band-pass; US cycles (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: Business cycle estimation with high-pass and band-pass local polynomial regression (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:5:y:2017:i:1:p:1-:d:86946
Access Statistics for this article
Econometrics is currently edited by Prof. Dr. Kerry Patterson
More articles in Econometrics from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().