Template-Type: ReDIF-Paper 1.0
Author-Name: Emanuel Kohlscheen
Author-X-Name-First: Emanuel 
Author-X-Name-Last: Kohlscheen
Title: What does machine learning say about the drivers of inflation?
Abstract: This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models, with a 28% RMSE reduction relative to a naïve AR(1) model and a 8% RMSE reduction relative to OLS. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.
Length: 22 pages
Creation-Date: 2021-11
File-URL: https://www.bis.org/publ/work980.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work980.htm
File-Format: text/html
Number: 980
Keywords: expectations, forecast, inflation, machine learning, oil price, output gap, Phillips curve
Classification-JEL: E27, E30, E31, E37, E52, F41
Handle: RePEc:bis:biswps:980