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UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators

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  • George Kapetanios
  • Fotis Papailias

Abstract

This paper constructs a coincident indicator for the UK employing–for the first time–a novel set of "faster" indicators recently published by the Office for National Statistics. Most of these variables were first released during the COVID-19 pandemic with the aim of facilitating economics and social research by more timely indicators. The empirical evidence suggests that a coincident indicator based on a novel weekly dataset successfully captures the economic conditions in real-time. We further use this indicator in an out-of-sample macroeconomic nowcasting exercise targeting monthly economic growth, prices and retail sales.

Suggested Citation

  • George Kapetanios & Fotis Papailias, 2021. "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-10, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2021-10
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    References listed on IDEAS

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    1. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    2. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    3. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    4. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    5. Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
    6. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    7. Camba-Méndez, Gonzalo & Kapetanios, George & Papailias, Fotis & Weale, Martin R., 2015. "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies," Working Paper Series 1773, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. George Kapetanios & Fotis Papailias, 2022. "Real Time Indicators During the COVID-19 Pandemic Individual Predictors & Selection," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-15, Economic Statistics Centre of Excellence (ESCoE).
    2. George Kapetanios & Fotis Papailias, 2022. "An Evaluation Framework for Targeted Indicators Aggregates vs. Disaggregates," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-17, Economic Statistics Centre of Excellence (ESCoE).
    3. George Kapetanios & Fotis Papailias, 2022. "A Quality Assessment Framework for Maintaining & Publishing New Indicators," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-18, Economic Statistics Centre of Excellence (ESCoE).
    4. Alex Botsis & Kevin Lee, 2022. "Nowcasting Using Firm-Level Survey Data; Tracking UK Output Fluctuations and Recessionary Events," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-20, Economic Statistics Centre of Excellence (ESCoE).

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    More about this item

    Keywords

    coincident indicators; covid-19; factor models; nowcasting;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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