Narrow Your Choices

Refine By Topic

Refine By Series

Refine By Language

Refine By Date

—

Refine By IMF Taxonomy

Refine By JEL Taxonomy

Refine by LOC Taxonomy

Refine By THEMA Taxonomy

  • Print
  • Save

Europe > Iceland

You are looking at 1 - 2 of 2 items for :

  • Type: Journal Issue x
  • Forecasting and Other Model Applications x
Clear All Modify Search
Cover IMF Working Papers
Housing Boom and Headline Inflation: Insights from Machine Learning
Yang Liu
,
Di Yang
, and
Mr. Yunhui Zhao
Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.
Keywords:
Housing Price Inflation; Rent; Owner-Occupied Housing; Machine Learning; Forecast; machine-learning model; machine learning method; housing boom; D. forecasting result; Inflation; Housing prices; Housing; Consumer price indexes; Global; Europe; Australia and New Zealand; North America; Caribbean; VAR model
Cover IMF Working Papers
The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data
Marijn A. Bolhuis
and
Brett Rayner
We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.
Keywords:
WP; country; algorithm; Machine learning; GDP growth; forecasts; panel data; pooling; proximate country; machine learning method; example country; macroeconomic aggregate; bias-variance tradeoff; country of interest; Production growth; Eastern Europe