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A Diffusion Index Analysis of the Argentinean Business Economic Cycle During the COVID-19 Pandemic

Author

Listed:
  • Pedro Elosegui

    (Central Bank of Argentina)

  • Mirta González

    (Central Bank of Argentina)

  • María Cecilia Pérez

    (Central Bank of Argentina)

  • Máximo Sangiácomo

    (Central Bank of Argentina)

Abstract

The Central Banks use diffusion indexes (DIs) to synthesize information from proprietary surveys that complement official statistics generating real time proxies of the economically relevant variables. According to the evidence, the DIs closely follow the economic cycle reflected in those official statistics. In this paper, the Survey of Business Economic Perspectives collected by the Central Bank of Argentina, is used to calculate two diffusion indexes: (i) the marginal diffusion index (MDI) based on the balance of answers and demeaned by the averaged participant response aiming at correcting for the “respondent bias” and a (ii) marginal fixed diffusion index (MFDI) that corrects the ex-post changes on past MDI index generated by changes in the average participant response. Both indexes are analyzed for the 2017-2022 period, a particularly volatile business cycle for Argentina and (given the impact of Covid-19) for the global economy. An econometric procedure aimed at assessing the indexes relationships with the official economic activity indicators is introduced. The analysis indicates that the DIs calculated with the BCRA’s Survey information closely follow and even anticipate the behavior of other official activity indicators both for the entire sample of firms and the industrial sector.

Suggested Citation

  • Pedro Elosegui & Mirta González & María Cecilia Pérez & Máximo Sangiácomo, 2022. "A Diffusion Index Analysis of the Argentinean Business Economic Cycle During the COVID-19 Pandemic," BCRA Working Paper Series 2022105, Central Bank of Argentina, Economic Research Department.
    • Handle: RePEc:bcr:wpaper:2022105
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    References listed on IDEAS

    as
    1. Santiago Pinto & Pierre-Daniel G. Sarte & Sonya Ravindranath Waddell, 2015. "Monitoring Economic Activity in Real Time Using Diffusion Indices: Evidence from the Fifth District," Economic Quarterly, Federal Reserve Bank of Richmond, issue 4Q, pages 275-301.
    2. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    3. 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.
    4. Jacob Berman & Scott Brave & Thomas Walstrum, 2015. "The Chicago Fed Survey of Business Conditions: Quantifying the Seventh District’s Beige Book Report," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q III.
    5. Santiago Pinto & Sonya Ravindranath Waddell, 2022. "Why Use a Diffusion Index?," Richmond Fed Economic Brief, Federal Reserve Bank of Richmond, vol. 22(22), June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    diffusion index; business cycle; economic activity;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • 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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