International Monetary Fund. Strategy, Policy, & Review Department
This paper presents traction as a multidimensional concept and discusses a comprehensive and complementary set of approaches to attempt to measure it based on the Fund’s value added to policy dialogue and formulation and public debate in member countries.
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.
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.