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Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data

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  • Tesi Aliaj
  • Milos Ciganovic
  • Massimiliano Tancioni

Abstract

We evaluate the predictive performances of the least absolute shrinkage and selection operator (Lasso) as an alternative shrinkage method for high‐dimensional vector autoregressions. The analysis extends the Lasso‐based multiple equations regularization to a mixed/high‐frequency data setting. Very short‐term forecasting (nowcasting) is used to target the Euro area's inflation rate. We show that this approach can outperform more standard nowcasting tools in the literature, producing nowcasts that closely follow actual data movements. The proposed tool can overcome information and policy decision problems related to the substantial publishing delays of macroeconomic aggregates.

Suggested Citation

  • Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:3:p:464-480
    DOI: 10.1002/for.2944
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    Cited by:

    1. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    2. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    3. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.

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