Learning About Informational Rigidities from Sectoral Data and Diffusion Indices

44 Pages Posted: 16 Dec 2012

Date Written: May 1, 2010

Abstract

This paper uses sectoral data to study survey-based diffusion indices designed to capture changes in the business cycle in real time. The empirical framework recognizes that when answering survey questions regarding their firm's output, respondents potentially rely on infrequently updated information. The analysis then suggests that their answers reflect considerable information lags, on the order of 16 months on average. Moreover, because information stickiness leads respondents to filter out noisy output fluctuations when answering surveys, it helps explain why diffusion indices successfully track business cycles and their consequent widespread use. Conversely, the analysis shows that in a world populated by fully informed identical firms, as in the standard RBC framework for example, diffusion indices would instead be degenerate. Finally, the data suggest that information regarding changes in aggregate output tends to be sectorally concentrated. The paper, therefore, is able to offer basic guidelines for the design of surveys used to construct diffusion indices.

Keywords: information stickiness, diffusion indices, approximate factor model

JEL Classification: E32, C42, C43

Suggested Citation

Sarte, Pierre-Daniel, Learning About Informational Rigidities from Sectoral Data and Diffusion Indices (May 1, 2010). FRB Richmond Working Paper No. 10-09. Available at SSRN: https://ssrn.com/abstract=2189552 or http://dx.doi.org/10.2139/ssrn.2189552

Pierre-Daniel Sarte (Contact Author)

Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

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