We find that countries which are able to borrow at spreads that seem low given fundamentals (for example because investors take a bullish view on a country's future), are more likely to develop economic difficulties later on. We obtain this result through a two-stage procedure, where a first regression links sovereign spreads to fundamentals, after which residuals from this regression are deployed in a second stage to assess their impact on future outcomes (real GDP growth and the occurrence of fiscal crises). We confirm the relevance of past sovereign debt mispricing in several out-of-sample exercises, where they reduce the RMSE of real GDP growth forecasts by as much as 15 percent. This provides strong support for theories of sentiment affecting the business cycle. Our findings also suggest that countries shouldn't solely rely on spread levels when determining their fiscal strategy; underlying fundamentals should inform policy as well, since historical relationships between spreads and fundamentals often continue to apply in the medium-to-long run.
Ramzy Al-Amine, Tim Willems, and Mr. Craig Beaumont
. While this likely means that our resulting model is not optimized, finding the optimal forecasting model is not our objective: we are merely interested in comparing three different models – and the absence of fixed effects across all competing specifications, does not favor one model over the others.
To compare the out-of-sample performance of the three competing models, we first implement a “leave-one-out” cross-validationprocedure which follows an iterative process where the estimation sample consists of all countries in the dataset except for country J
classifications using game theory . The Journal of Machine Learning Research , 11 : 1 – 18 , 2010 .
A RF hyper-parameters
To find the optimal hyper parameter m try I use the following repeated cross-validationprocedure:
1. The sample is divided into 10 random subsamples.
2. I guess a value for m try
3. For each subsample I estimate the RF model using all observations except that subsample and then use the held-out subsample to make out-of-sample predictions. Performance is measured as the average RMSE over all 10 subsamples.
4. Steps 2–3 is repeated
Kareem Ismail, Mr. Roberto Perrelli, and Jessie Yang
-than-usual gross financing needs, but it is also subject to riskier macroeconomic outcomes.
4 This requires merging 29 databases (one for each WEO vintage covered in this study).
5 The IMF’s Monitoring of Fund Arrangements (MONA) database can be accessed in the following address: https://www.imf.org/external/np/pdr/mona/index.aspx . A few measures were taken to ensure data consistency and quality control of MONA-based forecasts. Real GDP growth projections were manually corrected according to cross-validationprocedures that compared IMF program documents with the
Can countries improve their business climate through reforms in specific policy areas? Kraay and Tawara (2013) find that the answer depends on how we measure the business climate. When regressing seven different business climate indices on 38 policy indicators, they find little agreement among the seven models as to which of those policy indicators matter most. I revisit this puzzle using the same data but replacing their linear models with a Random Forest algorithm. I find a strong consensus across models on the importance ranking of policy indicators: No matter which business climate index is considered, the top ten contributors to a better business climate always include high recovery rates in insolvency proceedings (i.e., cents on the dollar for creditors), shorter border formalities for both importers and exporters, and low costs for starting a business. I show that the marginal effect of reforms is heterogeneous across countries and document how reform priorities depend on country specific circumstances.
Kareem Ismail, Mr. Roberto Perrelli, and Jessie Yang
Are IMF growth forecasts systematically optimistic? And if so, what is the role of planned policy adjustments on this outcome? Are program forecasts as biased as surveillance forecasts? We try to answer these questions using a comprehensive database on IMF forecasts of economic growth in surveillance and program cases during 2003–2017. We find that large planned fiscal and external adjustments are associated with optimistic growth projections, with significant non-linearities for both program and surveillance cases. Specifically, we find evidence that larger planned fiscal adjustment is associated with higher growth optimism in IMF non-concessional, non-precautionary financial arrangements. Our results show the tendency for optimism has persisted in the period after the Global Financial Crisis. Moreover, the strong correlation between the magnitude of the optimism and expected fiscal consolidation provides a cautionary signal for the post-COVID IMF projections as countries embark on a path of fiscal adjustment.
This paper assesses the roles of shocks, rules, and institutions as possible sources of procyclicality in fiscal policy. By employing parametric and nonparametric techniques, I reach the following four main conclusions. First, policymakers' reactions to the business cycle is different depending on the state of the economy-fiscal policy is "acyclical" during economic bad times, while it is largely procyclical during good times. Second, fiscal rules and fiscal responsibility laws tend to reduce the deficit bias on average, and seem to enhance, rather than to weaken, countercyclical policy. However, the evidence also suggests that fiscal frameworks do not exert independent effects when the quality of institutions is accounted for. Third, strong institutions are associated to a lower deficit bias, but their effect on procyclicality is different in good and bad times, and it is subject to decreasing returns. Fourth, unlike developed countries, fiscal policy in developing countries is procyclical even during (moderate) recessions; in "good times," however, fiscal policy is actually more procyclical in developed economies.
exclude a linear structure, but tests for it. Moreover, its forward and backward nature protects against over-fitting. The risk of obtaining a perfect fit to the sample data and poorly performing out-of-sample is avoided by the “crossvalidation” procedure: at each stage, the MSE of a candidate model is evaluated on a subset of sample observations that were not used in the estimation. An important property of adaptive splines is their capacity to effectively deal with heterogeneity and “outliers”: by isolating a local “sharp structures,” the fit in other regions of the