Signal extraction and estimation of a trend: a Monte Carlo study

✍ Scribed by Laurence Boone; Stephen G. Hall

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 131 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199903)18:2<129::aid-for718>3.0.co;2-9

✦ Synopsis

Several authors (King and Rebelo, 1993;

have questioned the use of exponentially weighted moving average ®lters such as the Hodrick±Prescott ®lter 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 dierent methods of estimation or other ways of de®ning 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 speci®c approach based on a more general ®lter, the stochastic trend model, that allows us to estimate all the parameters of the model rather than ®xing them arbitrarily, as is done with mainly of the commonly used ®lters. We illustrate the properties of the proposed technique relative to the conventional ones by employing a Monte Carlo study.