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Forecasting social unrest : a machine learning approach
Redl, Chris; Hlatshwayo, Sandile - 2021
We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial,...
Persistent link: https://www.econbiz.de/10012796240
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Improving the short-term forecast of world trade during the Covid-19 pandemic using SWIFT data on letters of credit
Carton, Benjamin; Hu, Nan; Mongardini, Joannes; Moriya, Kei - 2020
An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT … (PMI), to improve the short-term forecast of international trade. A horse race between linear regressions and machine …
Persistent link: https://www.econbiz.de/10012392595
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Predicting fiscal crises : a machine learning approach
Hellwig, Klaus-Peter - 2021
In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random...
Persistent link: https://www.econbiz.de/10012612343
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Nowcasting GDP : a scalable approach using DFM, machine learning and novel data, applied to European economies
Dauphin, Jean-François; Dybczak, Kamil; Maneely, Morgan; … - 2022
This paper describes recent work to strengthen nowcasting capacity at the IMF's European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning...
Persistent link: https://www.econbiz.de/10013169983
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unFEAR : unsupervised feature extraction clustering with an application to crisis regimes classification
Chan-Lau, Jorge A.; Wang, Ran - 2020
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...
Persistent link: https://www.econbiz.de/10012392653
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The more the merrier? : a machine learning algorithm for optimal pooling of panel data
Bolhuis, Marijn A.; Rayner, Brett - 2020
improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as …
Persistent link: https://www.econbiz.de/10012251287
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Deus ex machina? : a framework for macro forecasting with machine learning
Bolhuis, Marijn A.; Rayner, Brett - 2020
We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how … models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine … learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help …
Persistent link: https://www.econbiz.de/10012251288
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An algorithmic crystal ball : forecasts-based on machine learning
Jung, Jin-Kyu; Patnam, Manasa; Ter-Martirosyan, Anna - 2018
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying...
Persistent link: https://www.econbiz.de/10011932417
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Machine learning and causality : the impact of financial crises on growth
Tiffin, Andrew - 2019
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some...
Persistent link: https://www.econbiz.de/10012154563
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Foreign exchange intervention rule for central banks : risk-based framework
Lafarguette, Romain; Veyrune, Romain - 2021
This paper presents a rule for foreign exchange interventions (FXI), designed to preserve financial stability in floating exchange rate arrangements. The FXI rule addresses a market failure: the absence of hedging solution for tail exchange rate risk in the market (i.e. high volatility). Market...
Persistent link: https://www.econbiz.de/10012518276