How Well Do Diffusion Indexes Capture Business Cycles? A Spectral Analysis

By Owens, Raymond E.; Sarte, Pierre-Daniel G. | Economic Quarterly - Federal Reserve Bank of Richmond, Fall 2005 | Go to article overview

How Well Do Diffusion Indexes Capture Business Cycles? A Spectral Analysis


Owens, Raymond E., Sarte, Pierre-Daniel G., Economic Quarterly - Federal Reserve Bank of Richmond


Regional Federal Reserve banks expend considerable effort preparing for FOMC meetings, culminating in a statement presented to the committee. Statements typically begin with an assessment of regional economic conditions, followed by an update on national economic conditions and other developments pertinent to monetary policy.

This article examines whether the regional economic information produced by the Federal Reserve Bank of Richmond (FRBR), in the form of diffusion indexes, can be tied to the business cycle. Such a link is of direct interest because of its applicability to policy decisions. Very short cycles (such as a month in length) are potentially just noise and of little policy interest. Very long cycles (such as a long-term trend) are typically thought to be driven by technological considerations over which policy has little bearing. In contrast, one generally thinks of monetary policy decisions as affecting primarily medium-length cycles or business cycles. The objective of the research herein, therefore, is to identify which of the FRBR's indexes tend to reflect primarily business cycle considerations. Indeed, indexes for which such considerations are small or nonexistent have little hope of providing any information about the state of aggregate production measures over the business cycle, and their calculation would be of limited value.

At the regional level, economic data are less comprehensive and less timely than at the national level. For example, no timely data are published on statelevel manufacturing output or orders. In addition, published data on Gross State Product (GSP) are available with lags of 18 months or more. Also, these published data are available to FOMC members as soon as they are available to the Reserve Banks so that their analysis by the latter adds little to the broader monetary policy process. These shortcomings have led a number of organizations-including several regional Federal Reserve banks-to produce their own regional economic data. These efforts mostly have taken the form of high-frequency surveys. Surveys provide speed and versatility, overcoming the obstacles inherent in the traditional data. But surveys are often relatively expensive per respondent, leading organizations to maintain relatively small sample sizes. Further, to limit the burden on respondents, survey instruments often ask very simple questions, limiting the information set and level of analysis.

The Richmond Fed conducts monthly surveys of both manufacturing and services sector activity. The number of survey respondents is usually around 100 and respondents report mostly whether a set of measures increased, decreased, or was unchanged. However, there are several measures-primarily changes in prices-reported as an annual percentage change. Results from these surveys, along with Beige Book information, comprise the foundation of regional economic input into monetary policy discussions.

That said, there are several reasons why one may be skeptical of diffusion indexes' ability to capture useful variations in the business cycle. Specifically, the usefulness of diffusion indexes hinges critically on the following aspects of survey data:

* Diffusion indexes are produced from data collected at relatively high frequency-with new indexes being typically released every monthand therefore potentially quite noisy.

* The surveys must contain a large enough sample in order that a diffusion index capture potentially meaningful variations at business cycle frequencies. As a stark example, note that if only two firms were surveyed, the index I above would only ever take on five values, (-100,-50,0,50,

100}. If three firms were sampled, I in (1) would only ever take on the values {-100, -66, -33, 0, 33, 66, 100}. Evidently, / will take on more and more values the more firms are sampled. This may not be a problem for identifying whether the resulting index is driven mainly by business cycle considerations per se, but will affect the degree to which such indexes commove with more continuous aggregate measures of production over the cycle. …

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