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Middle East and Central Asia > Qatar

You are looking at 1 - 2 of 2 items for :

  • Type: Journal Issue x
  • Computable and Other Applied General Equilibrium Models x
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Cover IMF Working Papers
Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects
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.
Keywords:
Reinforcement learning; Deep reinforcement learning; Artificial intelligence, RL; DRL; Learning algorithms; Macro modeling; RL algorithm overview; trust region policy optimization; DRL algorithm; decision process; RL algorithm; Machine learning; Artificial intelligence; Debt relief; General equilibrium models; Global
Cover IMF Working Papers
Structural Reforms and Economic Growth: A Machine Learning Approach
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.
Keywords:
Structural reforms; institutions; economic growth; C. PLS estimation procedure; machine learning approach; Gabor pula; Liyang sun; labor market composite; Business environment; Labor markets; Machine learning; Labor market reforms; Global