Computers > Intelligence (AI) & Semantics

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Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
Mr. Philip Barrett and Jonathan J. Adams
The consensus among central bankers is that higher inflation expectations can drive up inflation today, requiring tighter policy. We assess this by devising a novel method for identifying shocks to inflation expectations, estimating a semi-structural VAR where an expectation shock is identified as that which causes measured expectations to diverge from rationality. Using data for the United States, we find that a positive inflation expectations shock is deflationary and contractionary: inflation, output, and interest rates all fall. These results are inconsistent with the standard New Keynesian model, which predicts inflation and interest rate hikes. We discuss possible resolutions to this new puzzle.
Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang
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 (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
José Garrido, Ms. Yan Liu, Joseph Sommer, and Juan Sebastián Viancha
This note explores the interactions between new technologies with key areas of commercial law and potential legal changes to respond to new developments in technology and businesses. Inspired by the Bali Fintech Agenda, this note argues that country authorities need to closely examine the adequacy of their legal frameworks to accommodate the use of new technologies and implement necessary legal reform so as to reap the benefits of fintech while mitigating risks. Given the cross-border nature of new technologies, international cooperation among all relevant stakeholders is critical. The note is structured as follows: Section II describes the relations between technology, business, and law, Section III discusses the nature and functions of commercial law; Section IV provides a brief overview of developments in fintech; Section V examines the interaction between technology and commercial law; and Section VI concludes with a preliminary agenda for legal reform to accommodate the use of new technologies.
Jiaxiong Yao and Mr. Yunhui Zhao
To reach the global net-zero goal, the level of carbon emissions has to fall substantially at speed rarely seen in history, highlighting the need to identify structural breaks in carbon emission patterns and understand forces that could bring about such breaks. In this paper, we identify and analyze structural breaks using machine learning methodologies. We find that downward trend shifts in carbon emissions since 1965 are rare, and most trend shifts are associated with non-climate structural factors (such as a change in the economic structure) rather than with climate policies. While we do not explicitly analyze the optimal mix between climate and non-climate policies, our findings highlight the importance of the nonclimate policies in reducing carbon emissions. On the methodology front, our paper contributes to the climate toolbox by identifying country-specific structural breaks in emissions for top 20 emitters based on a user-friendly machine-learning tool and interpreting the results using a decomposition of carbon emission ( Kaya Identity).
Katharina Bergant, Miss Anke Weber, and Andrea Medici
Using micro-data from household expenditure surveys, we document the evolution of consumption poverty in the United States over the last four decades. Employing a price index that appears appropriate for low income households, we show that poverty has not declined materially since the 1980s and even increased for the young. We then analyze which social and economic factors help explain the extent of poverty in the U.S. using probit, tobit, and machine learning techniques. Our results are threefold. First, we identify the poor as more likely to be minorities, with