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Econometrics 2017, 5(1), 1; doi:10.3390/econometrics5010001

Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression

Banco de España, Madrid 28014, Spain
Academic Editors: Gilles Dufrénot, Fredj Jawadi and Alexander Mihailov
Received: 30 June 2016 / Revised: 13 December 2016 / Accepted: 16 December 2016 / Published: 5 January 2017
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Abstract

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. View Full-Text
Keywords: business cycles; local polynomial regression; filtering; high-pass; band-pass; US cycles business cycles; local polynomial regression; filtering; high-pass; band-pass; US cycles
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Álvarez, L.J. Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression. Econometrics 2017, 5, 1.

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