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International Monetary Fund. Strategy, Policy, & Review Department
This paper presents traction as a multidimensional concept and discusses a comprehensive and complementary set of approaches to attempt to measure it based on the Fund’s value added to policy dialogue and formulation and public debate in member countries.
Mr. Jorge A Chan-Lau
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.
Marijn A. Bolhuis and Brett Rayner
We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.
Ms. Yuko Hashimoto and Mr. Konstantin Wacker
In this paper we investigate whether better information about the macroeconomic environment of an economy has a positive impact on its capital inflows, namely portfolio and foreign direct investment (FDI). The purpose of our study is to explicitly quantify information asymmetries by compliance with the IMF's Special Data Dissemination Standard (SDDS). For FDI, we find statistically significant and robust support for this hypothesis: SDDS subscription increased inflows by an economically relevant magnitude of about 60 percent. We also find evidence of aversion against political and macroeconomic risk as determinants of portfolio and FDI flows anduse a non-parametric test for spatial correlation in the residual of capital flows.
International Monetary Fund
This Report on the Observance of Standards and Codes on Data Module for Iceland highlights Data Module, response by the authorities, and detailed assessments using the data quality assessment framework. Iceland’s macroeconomic statistics are generally of high quality and are adequate to conduct effective surveillance. There is a high degree of quality awareness among Iceland’s statistical managers. There are some deficiencies in the periodicity and timeliness of the producer price index, and in the timeliness of central government finance statistics.