Search Results

You are looking at 1 - 10 of 17 items for :

  • "learning framework" x
Clear All
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.
Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao

more effective than traditional econometric methods . Machine learning models frequently employ techniques that are familiar to most economists; indeed, most machine-learning textbooks start with simple ordinary least squares (OLS) regression (see Appendix). However, the focus of machine-learning models is somewhat different from traditional econometric models. Instead of exploring issues of identification and causality, the primary focus of machine learning is on producing more precise out-of-sample predictions. To this end, a machine-learning framework will exploit

Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao

Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT : 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

International Monetary Fund. African Dept.

a carefully articulated implementation strategy and a comprehensive Monitoring, Evaluation, and Learning Framework. A robust learning and follow-up arrangement will enable us to understand how we will be progressing on delivering the plan, indicating important milestones achieved and mapping out lessons learned in the process. Through the Ministry of Planning and Economic Development, my Government has shifted from the traditional Poverty Reduction Strategy Paper model of orienting planning to people-centred, long-term development thinking in line with regional

Mr. Jorge A Chan-Lau

clustering method aimed at facilitating economic crisis prediction. The approach in unFEAR is quite different from that in other machine learning-based crisis prediction models. The latter adopt a supervised learning framework: at any time period, the models assign a crisis or no crisis label to a country’s observed economic and financial predictor data based on whether the observation was followed or not by a crisis n periods ahead. The reliance on labeled data gives rise to the biased label problem. Briefly, two countries characterized by similar economic and

Rusdu Saracoglu

marginal rates of substitution and transformation, moved slowly and therefore could be approximated as constant. Expectations of inflation, though, were modeled within an error-learning framework in which the public was assumed to form its expectations about future rates of inflation as a distributed lag of past rates of inflation. Much of the earlier econometric work has proceeded along these lines, differing mainly about the exact specification of the distributed lag that was presumed to generate the expectations of inflation and about the presence of other economic

International Monetary Fund

adjustment costs and the costs of being out of equilibrium, the stock of real (or nominal) money balances adjusts proportionally to the discrepancy between the demand for money and the actual supply. In other words, when there is a change in the demand for money, the public is assumed to adjust only part of the way in the same period. Complete adjustment, which is defined by the equality of demand for and supply of money, is thus achieved slowly over time. This partial-adjustment variant of the error-learning framework has become very popular, because it has a certain

Man-Keung Tang and Mr. Xiangrong Yu
This paper studies the role of central bank communication of its economic assessment in shaping inflation dynamics. Imperfect information about the central bank's assessment - or the basis for monetary policy decisions - could complicate the private sector's learning about its policy response function. We show how clear central bank communication, which facilitates agents' understanding of policy reasoning, could bring about less volatile inflation and interest rate dynamics, and afford the authorities with greater policy flexibility. We then estimate a simple monetary model to fit the Mexican economy, and use the suggested paramters to illustrate the model's quantitative implications in scenarios where the timing, nature and persistence of shocks are uncertain.
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.
Man-Keung Tang and Mr. Xiangrong Yu

′ = q t | t − 1 σ q 2 + i t − g x t E t PS π t + 1 q 2 σ v 2 1 σ q 2 + 1 q 2 σ v 2 and σ q 2 ’ = ( 1 σ q 2 + 1 q 2 σ v 2 ) − 1 Before turning to estimation, it is worthwhile to note that in contrast to perpetual learning framework where the private