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Dominant Drivers of National Inflation

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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:0112
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    File URL: https://hdl.handle.net/11250/3041831
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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.
    2. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    3. Diego R. Känzig & Maximilian Konradt, 2024. "Climate Policy and the Economy: Evidence from Europe’s Carbon Pricing Initiatives," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(3), pages 1081-1124, September.

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