Mr. Ananthakrishnan Prasad, Selim Elekdag, Mr. Phakawa Jeasakul, Romain Lafarguette, Mr. Adrian Alter, Alan Xiaochen Feng, and Changchun Wang
The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.
Mr. Tobias Adrian, Federico Grinberg, Nellie Liang, and Sheheryar Malik
Using panel quantile regressions for 11 advanced and 10 emerging market economies, we
show that the conditional distribution of GDP growth depends on financial conditions, with
growth-at-risk (GaR)—defined as growth at the lower 5th percentile—more responsive than
the median or upper percentiles. In addition, the term structure of GaR features an
intertemporal tradeoff: GaR is higher in the short run; but lower in the medium run when
initial financial conditions are loose relative to typical levels, and the tradeoff is amplified by
a credit boom. This shift in the growth distribution generally is not incorporated when
solving dynamic stochastic general equilibrium models with macrofinancial linkages, which
suggests downside risks to GDP growth are systematically underestimated.