A New Perspective of the Meese-Rogoff Puzzle: Application of Sparse Dynamic Shrinkage

TL;DR

This paper introduces the Markov Switching Dynamic Shrinkage process (MSDSP), a flexible Bayesian model that improves exchange rate predictions and offers a new perspective on the Meese-Rogoff puzzle by outperforming the random walk model.

Contribution

The paper develops the MSDSP, a novel model combining sparsity, dynamic shrinkage, and structural change, and demonstrates its effectiveness in exchange rate prediction and economic model evaluation.

Findings

MSDSP outperforms the random walk model in exchange rate forecasting.

Enhanced economic models with MSDSP produce superior predictive distributions.

The approach provides new insights into the Meese-Rogoff puzzle.

Abstract

We propose the Markov Switching Dynamic Shrinkage process (MSDSP), nesting the Dynamic Shrinkage Process (DSP) of Kowal et al. (2019). We revisit the Meese-Rogoff puzzle (Meese and Rogoff, 1983a,b, 1988) by applying the MSDSP to the economic models deemed inferior to the random walk model for exchange rate predictions. The flexibility of the MSDSP model captures the possibility of zero coefficients (sparsity), constant coefficient (dynamic shrinkage), as well as sudden and gradual parameter movements (structural change) in the time-varying parameter model setting. We also apply MSDSP in the context of Bayesian predictive synthesis (BPS) (McAlinn and West, 2019), where dynamic combination schemes exploit the information from the alternative economic models. Our analysis provide a new perspective to the Meese-Rogoff puzzle, illustrating that the economic models, enhanced with the parameter flexibility of the MSDSP, produce predictive distributions that are superior to the random walk model, even when stochastic volatility is considered.