EMPIRICAL PERFORMANCE OF POTENTIAL GDP AND OUTPUT GAP ESTIMATION METHODS: A CRITICAL REVIEW WITH EVIDENCE FROM EMERGING AND ADVANCED ECONOMIES

Abstract

Potential GDP and the output gap are estimated using methods ranging from univariate filters to Bayesian unobserved-components (UC) models; however, these approaches are often treated as interchangeable in empirical research and policy analysis. This study critically reviews twelve peer-reviewed studies that apply and compare these methods using real-world data from advanced economies (the United States and the euro area) and emerging markets (Turkey and Israel). The analysis examines the extent to which the methods converge or diverge in estimating the amplitude, cyclical turning points, and revision behaviour of the output gap, as well as the underlying reasons for these differences. The strongest agreement is observed in identifying the timing of major cyclical turning points, whereas the greatest discrepancies arise in the estimated amplitude and persistence of the output gap. Three methodological factors account for most of these differences: the assumed signal-to-noise ratio of the cyclical component; whether the trend specification permits structural breaks or time-varying growth; and the choice of auxiliary variables used to identify the trend-cycle decomposition. Although the evidence from emerging markets is limited to two country cases, it indicates that these divergences become more pronounced in economies characterised by higher volatility and ongoing structural change, highlighting the need for further research extending multivariate and Bayesian approaches to developing countries. The review concludes with practical recommendations for researchers and policymakers regarding the selection, interpretation, and reporting of output gap estimates.