Macroeconomic Forecasting using Low†frequency Filters

João Valle e Azevedo and Ana Pereira

Oxford Bulletin of Economics and Statistics, 2018, vol. 80, issue 1, 39-64

Abstract: We consider univariate low†frequency filters applicable in real†time as a macroeconomic forecasting method. This amounts to targeting only low frequency fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains in forecast accuracy can be obtained in comparison with a variety of other methods. There is an inherent arbitrariness in the choice of the cut†off defining low and high frequencies, which calls for a careful characterization of the implied optimal (for forecasting) degree of smoothing of the key macroeconomic indicators we analyse. We document interesting patterns that emerge: for most variables the optimal choice amounts to disregarding fluctuations well below the standard business cycle cut†off of 32 quarters while generally increasing with the forecast horizon; for inflation and variables related to housing this cut†off lies around 32 quarters for all horizons, which is below the optimal level for federal government spending.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/obes.12194

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:bla:obuest:v:80:y:2018:i:1:p:39-64

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049

Access Statistics for this article

Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple

More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().