IMF Working Papers

Nowcashing: Using Daily Fiscal Data for Real-Time Macroeconomic Analysis

By Florian Misch, Brian Olden, Marcos Poplawski Ribeiro, Lamya Kejji

November 6, 2017

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Florian Misch, Brian Olden, Marcos Poplawski Ribeiro, and Lamya Kejji. Nowcashing: Using Daily Fiscal Data for Real-Time Macroeconomic Analysis, (USA: International Monetary Fund, 2017) accessed November 8, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Traditionally, fiscal data for policy analysis are derived from official reports that, depending on the country, are published either monthly, quarterly or annually, often with significant time lags. However, innovations in digitalization of government payments and accounting systems mean that real-time daily fiscal data exist in many countries. In this paper, we argue that these data contain valuable, but underutilized and underexploited information. Possible uses include (i) realtime fiscal surveillance which allows for much more timely responses to emerging signs of fiscal stress, and (ii) nowcasting economic activity, which is especially useful in countries where higher frequency GDP statistics are unavailable.

Subject: Currencies, Economic and financial statistics, Expenditure, Government finance statistics, Money, PFM information systems, Public financial management (PFM), Revenue administration

Keywords: Balance, Balances data, Cash balance, Country, Currencies, Economic activity, Fiscal policy, Global, Government, Government bank accounts, Government banking arrangement, Government finance statistics, Government payment system, Nowcashing, Nowcasting, PFM information systems, Real-time data, WP

Publication Details

  • Pages:

    29

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2017/227

  • Stock No:

    WPIEA2017227

  • ISBN:

    9781484325933

  • ISSN:

    1018-5941

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