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See fuller discussion of this distinction in Ghosh et al. (2009) and IMF (2007).


For more on the types of Fund surveillance touching on risk topics see IEO (2014), including Chapter 3 section C, and background paper IV, IMF (2014b), including Chapter 1 of the background studies.


Previously, the apparatus was referred to as the Early Warning Exercise (EWE). Since 2010, the EWE has been recast as a separate and distinct exercise.


Papers documenting some of this evolution include IMF (2007, 2010, and 2011).


See for example IMF (2013).


For instance, empirical analysis suggests that equity and property price gaps tend to peak one and two years ahead of a crisis, respectively, making them potentially useful as leading indicators.


See IMF (2014a), Appendix II, for a defnition and list of frontier market economies.


See Dabla-Norris and Gunduz (2012) for further technical details.


The EBA is a suite of assessment methods. The principal methods are panel regression-based, taking into account a broad set of factors—and policies—that may infuence the current account and real exchange rate. In the frst stage, these regression methods focus on understanding current account and real exchange rate developments. The second stage estimates the contributions of several “policy gaps” to current accounts and real exchange rates, providing a normative evaluation. In addition, EBA retains from its predecessor, the CGER, a model-free method focused on sustainability analysis. Here, current account imbalances are assessed as the difference between the current account balance needed to stabilize net foreign asset position at a benchmark level and the medium-term projection for the current account in the WEO. The exchange rate gap is the estimated change in the exchange rate needed to achieve the stabilizing current account balance. For more details on EBA, see Philips et al. (2013). and on CGER, see Lee et al. (2008).


This model’s forecast has been shown to outperform those from random walk and standard VAR models over a long period.


For a full description of the growth tracking model see Matheson (2011).


This metric is motivated by the work in Alper et al. (2012).


A fscal reaction function is estimated, establishing the relationship between a country’s primary balance and the output gap, the debt level, and previous primary balances. A VAR is estimated to capture future variability in macroeconomic outcomes. The VAR is then simulated going forward with each country’s debt path determined by their fscal reaction function. Debt vulnerability is assessed as the probability that debt levels exceed an established threshold over a fve-year horizon. The methodology follows the stochastic simulations approach of Celasun et al. (2006), which marries the approach to fscal reaction functions in Abiad and Ostry (2005), and the stochastic analysis of debt issues in Garcia and Rigobon (2005).


Assuming no impact on potential GDP and no countervailing discretionary fscal action, the shock affects the fscal balance and debt-to-GDP ratios through automatic stabilizers and change in the GDP base. In countries with large debt-to-GDP ratios, the GDP base effects would explain the deterioration of debt dynamics well, while in countries with lower debt but relatively large expenditure ratios, automatic stabilizers would worsen the fscal balances, and in turn, debt.


This composite measure is based on (1) the probability of the interbank market being in a low, medium, or high volatility state as estimated by a Markov regime switching model, and (2) whether the level and volatility of the spread is high relative to the pre-crisis mean.


Misalignments in house prices relative to fundamentals are estimated using an error correction model, relating short-term changes in house prices to a long term equilibrium relationship, interest rates, and to changes in income per capita, credit growth, equity prices, and the fraction of working-age population, with consideration given to supply side factors. These estimates are supplemented by deviations of price-to-income and price-to-rent ratios from their historical averages.


A household indebtedness index is measured based on household credit to GDP levels and past two years’ household credit growth rates. The index is then compared with values during boom phases that precede a bust.


Based on characteristics such as loan-to-value (LTV) ratios, share of variable rate mortgages, and recourse law (the absence of which is typically associated with higher default risk).


These risks are presented in the Risk Assessment Matrices (RAMs) in IMF country staff reports.


Simulation results are extended to countries outside the sample of the models used depending on the model and the structure of the economy in question. The impact is extrapolated using elasticities estimated from regression analyses tailored to the variable in question.

Assessing Country Risk: Selected Approaches
Author: Mr. Ashvin Ahuja, Kevin Wiseman, and Mr. Murtaza H Syed