Dominant Drivers of National Inflation

TL;DR

This paper introduces D2ML, a novel machine learning approach combining graphical models and time-dependent data to identify key drivers of national inflation, demonstrated on 33 countries with US inflation and oil prices as primary factors.

Contribution

The paper presents D2ML, a new method integrating model selection, graphical models, and time series analysis to identify inflation drivers, validated through simulations and real-world data.

Findings

US inflation rate is a dominant driver of national inflation.

Oil prices significantly influence inflation across countries.

The estimator accurately identifies drivers in simulated environments.

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.