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Dominant Drivers of National Inflation

Author

Listed:
  • Jan Ditzen
  • Francesco Ravazzolo

Abstract

For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation. D2ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national inflation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers.

Suggested Citation

  • Jan Ditzen & Francesco Ravazzolo, 2022. "Dominant Drivers of National Inflation," Working Papers No 08/2022, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    • Handle: RePEc:bny:wpaper:0111
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    File URL: https://hdl.handle.net/11250/3041831
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    References listed on IDEAS

    as
    1. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "What Drives Oil Prices? Emerging Versus Developed Economies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1013-1028, November.
    2. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    3. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
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    Cited by:

    1. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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