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Signal extraction and estimation of a trend: a Monte Carlo study
Author(s) -
Boone Laurence,
Hall Stephen G.
Publication year - 1999
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199903)18:2<129::aid-for718>3.0.co;2-9
Subject(s) - monte carlo method , hodrick–prescott filter , spurious relationship , stylized fact , econometrics , computer science , filter (signal processing) , series (stratigraphy) , algorithm , statistics , mathematics , business cycle , economics , computer vision , macroeconomics , paleontology , biology , keynesian economics
Several authors (King and Rebelo, 1993; Cogley and Nason, 1995) have questioned the use of exponentially weighted moving average filters such as the Hodrick–Prescott filter in decomposing a series into a trend and cycle, claiming that they lead to the observation of spurious or induced cycles and to misinterpretation of stylized facts. However, little has been done to propose different methods of estimation or other ways of defining trend extraction. This paper has two main contributions. First, we suggest that the decomposition between the trend and cycle has not been done in an appropriate way. Second, we argue for a general to specific approach based on a more general filter, the stochastic trend model, that allows us to estimate all the parameters of the model rather than fixing them arbitrarily, as is done with mainly of the commonly used filters. We illustrate the properties of the proposed technique relative to the conventional ones by employing a Monte Carlo study. Copyright © 1999 John Wiley & Sons, Ltd.