Pegs, Floats, and Forests: A Machine Learning Revisit of Exchange Rate Regimes and Growth in Transition Economies
Marjan Petreski

TL;DR
This study uses machine learning and econometrics to analyze how exchange rate regimes affect growth in transition economies, highlighting institutional strength as a key factor.
Contribution
It combines traditional econometrics with random forest methods to validate and deepen understanding of exchange rate regime impacts on growth.
Findings
Intermediate regimes underperform fixed regimes with growth penalties of -1.0 to -10.4 percentage points.
Floating regimes show negative but largely insignificant growth differentials.
Institutional capacity influences the effectiveness of exchange rate anchoring.
Abstract
This paper combines traditional panel econometrics with random forest machine learning to revisit the relationship between exchange rate regimes and economic growth for 27 transition economies over 1991-2019. Exploiting the Couharde-Grekou (2024) probabilistic synthesis classification, the random forest approach non-parametrically confirms and sharpens what fixed-effects and system GMM estimation establish parametrically intermediate exchange rate regimes consistently underperform fixed arrangements, with growth penalties ranging from -1.0 to -10.4 percentage points, while floating regimes show negative but largely insignificant differentials. Beyond regime effects, the machine learning analysis reveals that the intermediate regime penalty is sharpest precisely where institutions are weakest - non-parametric validation that institutional capacity, not regime label alone, determines…
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