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The Information Content and Statistical Properties of Diffusion Indexes

Author

Listed:
  • Santiago Pinto

    (Federal Reserve Bank of Richmond)

  • Pierre-Daniel Sarte

    (Federal Reserve Bank of Richmond)

  • Robert Sharp

    (Uber)

Abstract

We study diffusion indexes constructed from qualitative surveys to provide real-time assessments of various aspects of economic activity. In particular, we highlight the role of diffusion indexes as estimates of change in a quasi-extensive margin, and characterize their distribution, focusing on the uncertainty implied by both sampling and the polarization of participants' responses. Because qualitative tendency surveys generally cover multiple questions around a topic, a key aspect of this uncertainty concerns the coincidence of responses, or the degree to which polarization co-moves, across individual questions. We illustrate these results using microdata on individual responses underlying different composite indexes published by the Michigan Survey of Consumers. We find a secular rise in consumer uncertainty starting around 2000, following a decade-long decline, and higher agreement among respondents in prior periods. In 2014, six years after the Great Recession, uncertainty arising from the polarization of responses in the Michigan Survey stood at its highest level, coinciding with the weakest recovery in U.S. postwar history. The formulas we derive allow for simple computations of approximate confidence intervals, thus affording a more complete real-time assessment of economic conditions using qualitative surveys.

Suggested Citation

  • Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.
  • Handle: RePEc:ijc:ijcjou:y:2020:q:3:a:2
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    References listed on IDEAS

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    Cited by:

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    3. NAKAJIMA, Jouchi, 2023. "Estimation of firms' inflation expectations using the survey DI," Discussion Paper Series 749, Institute of Economic Research, Hitotsubashi University.
    4. Petar Soric & Oscar Claveria, 2021. ""Employment uncertainty a year after the irruption of the covid-19 pandemic"," IREA Working Papers 202112, University of Barcelona, Research Institute of Applied Economics, revised May 2021.
    5. Oscar Claveria & Petar Sorić, 2023. "Labour market uncertainty after the irruption of COVID-19," Empirical Economics, Springer, vol. 64(4), pages 1897-1945, April.
    6. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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