Year: 2006
Type: report
Abstract: We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series.The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series.The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models.We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.
DOI: 10.3386/w12671
Authors: Ulrich Mueller, Mark Watson
Institutions: National Bureau of Economic Research, Princeton University
Language: English
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2103929465