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Article

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
Econometrics 2017, 5(1), 1; https://doi.org/10.3390/econometrics5010001
Received: 30 June 2016 / Revised: 13 December 2016 / Accepted: 16 December 2016 / Published: 5 January 2017
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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MDPI and ACS Style

Álvarez, L.J. Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression. Econometrics 2017, 5, 1. https://doi.org/10.3390/econometrics5010001

AMA Style

Álvarez LJ. Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression. Econometrics. 2017; 5(1):1. https://doi.org/10.3390/econometrics5010001

Chicago/Turabian Style

Álvarez, Luis J. 2017. "Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression" Econometrics 5, no. 1: 1. https://doi.org/10.3390/econometrics5010001

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