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Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?

Author

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  • Manu Sharma

    (Indian Institute of Technology (IIT) Bombay
    Department of Supervision, Reserve Bank of India)

  • Vinish Kathuria

    (Indian Institute of Technology (IIT) Bombay
    Institute of Development Studies)

Abstract

Moving beyond the crossroads of Earth Sciences and Environmental Studies, nowcasting has captured the interest of Central Bank researchers in the domain of Economics and Finance. Nowcasting offers a viable solution to the common challenges in forecasting, like delayed access to macroeconomic data, uneven timing of official data releases, structural shifts in the data generating process, and mismeasurement due to data gaps and revisions. The strength of nowcasts lies in their ability to update themselves based on the new information arriving regularly. However, a trade-off exists between the frequency of data arrival and the stability of nowcasts. This study examines the extant literature using a Structured Literature Review, with a special focus on Economics and Finance. The study highlights the needs and challenges in nowcasting and conducts a bibliometric analysis to identify the clustering patterns in keywords pertaining to nowcasting literature and their evolution over time, indicating the direction in which nowcasting research is heading. The study then identifies various models used in the literature, classifying them into a few overarching categories. The study finds that the principal advantage of standard statistical nowcasting models lies in their ability to handle the data issues such as high dimensionality, unavailability/lagged availability, non-synchronicity, non-linearity and frequent revisions in data. However, these models may not be appropriate for big data which witness exponential rise in hyperparameters. Machine learning models provide a complementing tool because they deal with big data and enable better cross-validation for efficient hyperparameter selection. Finally, this study undertakes an empirical exercise wherein Factor Augmented Mixed Data Sampling Model has been utilised to nowcast the United Kingdom’s inflation using high frequency indicators from December, 2021 to March, 2024—a period of turbulence when the country witnessed the worst bout of inflation. Nowcasts seem to perform better in predicting UK inflation during this period.

Suggested Citation

  • Manu Sharma & Vinish Kathuria, 2025. "Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(2), pages 333-388, June.
    • Handle: RePEc:spr:jqecon:v:23:y:2025:i:2:d:10.1007_s40953-024-00421-x
    DOI: 10.1007/s40953-024-00421-x
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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