Computers > Intelligence (AI) & Semantics

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Mr. Jorge A Chan-Lau, Ruofei Hu, Maksym Ivanyna, Ritong Qu, and Cheng Zhong
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Tohid Atashbar and Rui Aruhan Shi
This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The objective of the deterministic environment is to compare the learning agent's behavior to a deterministic steady-state scenario. We demonstrate that in both deterministic and stochastic scenarios, the agent's choices are close to their optimal value. We also present cases of unstable learning behaviours. This AI-macro model may be enhanced in future research by adding additional variables or sectors to the model or by incorporating different DRL algorithms.
Tohid Atashbar and Rui Aruhan Shi
The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
Mr. Anil Ari, Gabor Pula, and Liyang Sun
The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of cross-country structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.