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Macroeconomic Forecasting using Low‐frequency Filters
Author(s) -
Valle e Azevedo João,
Pereira Ana
Publication year - 2018
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12194
Subject(s) - inflation (cosmology) , univariate , econometrics , hodrick–prescott filter , economics , business cycle , smoothing , monetary policy , arbitrariness , statistics , mathematics , multivariate statistics , macroeconomics , linguistics , physics , philosophy , theoretical physics
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.