Fiscal crises

Medas, Paulo; Poghosyan, Tigran; Xu, Yizhi; Farah-Yacoub, Juan; Gerling, Kerstin

doi:10.1016/j.jimonfin.2018.08.001

Cited by 35 publications

(41 citation statements)

References 31 publications

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“…The attention is no longer on external defaults alone, but papers also acknowledge that fiscal crises may reflect other types of distress and affect both external and domestic creditors. Hence, the crisis definitions now include debt defaults (mainly external), IMF programs, implicit debt defaults (high inflation, domestic arrears), and loss of market access (Medas et al 2018;Bruns and Poghosyan 2018;. A broader set of methodologies is also used, although the logit model remains a popular approach.…”

Section: Onwards: a Growing Debate On The Role Of Public Debtmentioning

confidence: 99%

“…In some instances, however, heightened budgetary distress may be associated with domestic arrears or inflation (Reinhart and Rogoff 2011b), or a default is avoided thanks to official creditor assistance (Manasse, Roubini, and Schimmelpfennig 2003). To capture these different facets, we follow Medas et al (2018) and ©International Monetary Fund. Not for Redistribution identify fiscal crises in any given year if any of the following four criteria is met (for details, see Appendix 2):…”

Section: A Measuring Fiscal Crisesmentioning

confidence: 99%

“…INTRODUCTION Should governments worry about high public debt? The answer from standard debt sustainability frameworks is yes, for not only does real economic growth dip in the immediate aftermath of a fiscal crisis, but the loss of output is often permanent (Medas et al 2018;Asonuma et al 2019). However, despite Reinhart andRogoff's (2009 and2011a) seminal work on the perils of excessive debt, the empirical literature on the relationship between public debt and fiscal crises is still surprisingly inconclusive to this day.…”

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confidence: 99%

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Debt Is Not Free

Badia¹,

Medas²,

Gupta³

et al. 2020

IMF Working Papers

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With public debt soaring across the world, a growing concern is whether current debt levels are a harbinger of fiscal crises, thereby restricting the policy space in a downturn. The empirical evidence to date is however inconclusive, and the true cost of debt may be overstated if interest rates remain low. To shed light into this debate, this paper re-examines the importance of public debt as a leading indicator of fiscal crises using machine learning techniques to account for complex interactions previously ignored in the literature. We find that public debt is the most important predictor of crises, showing strong non-linearities. Moreover, beyond certain debt levels, the likelihood of crises increases sharply regardless of the interest-growth differential. Our analysis also reveals that the interactions of public debt with inflation and external imbalances can be as important as debt levels. These results, while not necessarily implying causality, show governments should be wary of high public debt even when borrowing costs seem low.

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Section: Onwards: a Growing Debate On The Role Of Public Debtmentioning

confidence: 99%

Section: A Measuring Fiscal Crisesmentioning

confidence: 99%

mentioning

confidence: 99%

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Debt Is Not Free

Badia¹,

Medas²,

Gupta³

et al. 2020

IMF Working Papers

Self Cite

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“…However, if GDP denotes rich destination countries irrespective of the prevailing fiscal condition and unemployment rates, there can be a negative link between favorable decisions and GDP level. In addition to GDP, we include a binary variable denoting fiscal crisis, that is, periods of extreme fiscal distress when governments have not been able to contain large fiscal imbalances, leading to the adoption of extreme measures such as debt default and monetization of the deficit (Medas et al, 2018).…”

Section: Explanatory Variablesmentioning

confidence: 99%

Understanding Decisions Made on Asylum Applications in Host Countries

Issifou

2020

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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“…A data point is labeled as a crisis if an economic crisis affects the country two years after the data point is observed and recorded. The crisis labels correspond to one of each of the following categories: external crisis, as defined in Basu et al (2017); financial crisis, as defined in Laeven and Valencia (2017); fiscal crisis, as defined in Medas et al (2018); and real sector crisis, as defined in Basu e et al (2017). The crisis/no-crisis labels are not used to find the economic crisis clusters to avoid the biased label problem.…”

Section: Technical Detailsmentioning

confidence: 99%

UnFEAR

Chan‐Lau¹,

Wang²

2020

IMF Working Papers

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We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.

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Fiscal crises

Cited by 35 publications

References 31 publications

Debt Is Not Free

Debt Is Not Free

Understanding Decisions Made on Asylum Applications in Host Countries

UnFEAR

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