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Exploring the use of anonymized consumer credit information to estimate economic conditions: an application of big data

Author

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  • Stephanie M. Wilshusen

Abstract

The emergence of high-frequency administrative data and other big data offers an opportunity for improvements to economic forecasting models. This paper considers the potential advantages and limitations of using information contained in anonymized consumer credit reports for improving estimates of current and future economic conditions for various geographic areas and demographic markets. Aggregate consumer credit information is found to be correlated with macroeconomic variables such as gross domestic product, retail sales, and employment and can serve as leading indicators such that lagged values of consumer credit variables can improve the accuracy of forecasts of these macro variables.

Suggested Citation

  • Stephanie M. Wilshusen, 2015. "Exploring the use of anonymized consumer credit information to estimate economic conditions: an application of big data," Consumer Finance Institute discussion papers 15-5, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpdp:15-05
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    File URL: https://www.philadelphiafed.org/consumer-finance/consumer-credit/exploring-the-use-of-anonymized-consumer-credit-information-to-estimate-economic-conditions
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    File URL: https://www.philadelphiafed.org/-/media/frbp/assets/consumer-finance/discussion-papers/dp15-05.pdf
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    Citations

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    Cited by:

    1. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    2. Dean Croushore & Stephanie M. Wilshusen, 2020. "Forecasting Consumption Spending Using Credit Bureau Data," Working Papers 20-22, Federal Reserve Bank of Philadelphia.

    More about this item

    Keywords

    Consumer credit information; Administrative data; Big data; Real-time data; Nowcasting; Forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

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