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Abstract:
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The detection and estimation of business cycles in economic time series is an important activity, and typically involves the filtering of seasonally adjusted time series. Econometricians favoring univariate model-based approaches to cycle estimation seek to avoid the identification of spurious cycles; however, given that seasonal adjustment is a procedure that greatly affects all frequencies, can cycles be adequately detected from raw data? Does seasonal adjustment generate spurious cycles? This paper seeks to provide statistical methodology that can be used to answer these queries. We introduce a diagnostic statistic for deciding the inclusion or exclusion of an unobserved component, such as a cycle, and determine its theoretical properties. We then describe how this can be used to address our research questions in a rigorous fashion, and how currently available tools are not adequate.
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