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A statistical approach to identifying current leading indicators of the US recovery

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  • Kieran Kelliher

Abstract

This paper employs a statistical approach to identify which sectors of the US economy are currently the most suitable indicators for monitoring the recovery. A comprehensive list of potential leading indicators is constructed through the application of economic theory. By applying a detailed evaluation and selection procedure, the most suitable of these indicators is identified. Principal component analysis is then utilised to construct eight summary indicators representative of the economic sectors which are considered to be strategic processes in the US business cycle. Subsequently, the summary indicators were used to generate forecasts of future GDP. The research identifies that the labour, external and industry sectors should currently be monitored in order to gauge the pace of the US recovery and to forecast the next economic downturn. This study highlights that the use of several leading indices, which are representative of important sectors of the economy, is more effective than a single economy-wide combination forecast and that these leading indices must be regularly reviewed in order to accurately forecast future economic activity.

Suggested Citation

  • Kieran Kelliher, 2013. "A statistical approach to identifying current leading indicators of the US recovery," International Journal of Public Policy, Inderscience Enterprises Ltd, vol. 9(1/2), pages 86-107.
    • Handle: RePEc:ids:ijpubp:v:9:y:2013:i:1/2:p:86-107
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    Cited by:

    1. Jacques Bughin, 2023. "Are you resilient? Machine learning prediction of corporate rebound out of the Covid‐19 pandemic," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(3), pages 1547-1564, April.

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