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Sparse Warcasting

Author

Listed:
  • Mihnea Constantinescu

    (National Bank of Ukraine
    University of Amsterdam)

Abstract

Forecasting economic activity during an invasion is a nontrivial exercise. The lack of timely statistical data and the expected nonlinear effect of military action challenge the use of established nowcasting and short-term forecasting methodologies. This study explores the use of Partial Least Squares (PLS) augmented with an additional sparsity step to nowcast quarterly Ukrainian GDP using Google search data. Model outputs are benchmarked against both static and dynamic factor models. Preliminary results outline the usefulness of PLS in capturing the effects of large shocks in a setting rich in data, but poor in statistics.

Suggested Citation

  • Mihnea Constantinescu, 2023. "Sparse Warcasting," Working Papers 01/2023, National Bank of Ukraine.
    • Handle: RePEc:ukb:wpaper:01/2023
    as

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    File URL: https://bank.gov.ua/admin_uploads/article/WP_2023-01_Constantinescu.pdf
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    References listed on IDEAS

    as
    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
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    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
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    7. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    8. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    nowcasting; quarterly GDP; Google Trends; machine learning; partial; least squares; sparsity; Markov blanket;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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