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International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
International Monetary Fund. Strategy, Policy, & Review Department

: Risk Assessment, Supervised Machine Learning, Prediction, Sudden Stop, Exchange Market Pressure, Fiscal Crisis, Debt, Financial Crisis, Economic Crisis, Economic Growth Corresponding Authors’ E-Mail Addresses: Daria Zakharova ( DZakharova@imf.org ) Wojciech Maliszewski ( WMaliszewski@imf.org ) Kevin Wiseman ( KWiseman@imf.org ) Andrew Tiffin ( ATiffin@imf.org ) Roberto Perrelli (External Sector) ( RPerrelli@imf.org ) Klaus Hellwig (Fiscal Sector) ( KHellwig@imf.org ) Jorge Chan-Lau (Real Sector) ( JChanLau@imf.org ) Publication orders may be

International Monetary Fund. Statistics Dept.

product labels can also be used for the classification using machine learning and text mining techniques . Nevertheless, it is recommended to use supervised machine learning for which the learning dataset is essential. Recommended Action: • Create a conversion table to link the product code to the National breakdown of the COICOP. C. Methodology 16. The turnover and the quantities of the weekly data are summed for the three weeks by product code and district . After the unit price is calculated by dividing the turnover by the quantities. 17. There are

International Monetary Fund. Statistics Dept.
The purpose of the mission was to assist the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) with continuing its modernization of the Consumer Price Index (CPI). This was the first technical assistance (TA) mission to Kazakhstan on scanner data (SD). The mission was delivered remotely.
Mr. John D Brondolo, Annette Chooi, Trevor Schloss, and Anthony Siouclis

alongside other mathematical approaches . These additional mathematic approaches include calculus, probability theory, and linear programming and optimization. Machine learning is often seen as a branch of artificial intelligence in that it involves machines “learning” from data. The machines can be “trained” to detect patterns, improve over time with experience, and make decisions without being explicitly programmed. Machine learning can be supervised or unsupervised . Supervised machine learning involves training the algorithms using past outcomes. In CRM, this

Majid Bazarbash
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Mr. Andrew J Tiffin
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 leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Mr. John D Brondolo, Annette Chooi, Trevor Schloss, and Anthony Siouclis
All tax administrations seek to maximize the overall level of compliance with tax laws. Compliance improvement plans (CIPs) are a valuable tool for increasing taxpayers’ compliance and boosting tax revenue. This note is intended to help tax administrations develop a CIP, by providing guidance on the following issues: (1) how to identify and rate compliance risks; (2) how to treat risks to achieve the best possible outcome; and (3) how to measure the impacts that treatments have had on compliance outcomes.
Jelle Barkema, Mr. Mico Mrkaic, and Yuanchen Yang

and easiest models for textual analysis. Therefore, we use LR as our baseline model, complemented by the support-vector-machine (SVM) model and the random forest (RF) model. The LR is a probabilistic classifier that relies on supervised machine learning. Its goal is to train a classifier that can make a binary decision about the class of a new input observation, which in our case is to decide whether a paragraph is about spillovers or not. Consider an input paragraph x, which is typically vectorized and represented as [x 1 , x 2 ,..., x n ]. The classifier output

Jelle Barkema, Mr. Mico Mrkaic, and Yuanchen Yang
This paper dives into the Fund’s historical coverage of cross-border spillovers in its surveillance. We use a state-of-the-art deep learning model to analyze the discussion of spillovers in all IMF Article IV staff reports between 2010 and 2019. We find that overall, while the discussion of spillovers decreased over time, it was pronounced in the staff reports of some systemically important economies and during periods of global spillover events. Spillover discussions were more prominent in staff reports covering advanced and emerging market economies, possibly reflecting their role as sources of global spillovers. The coverage of spillovers was higher in the context of the real, financial, and external sectors. Also, countries with larger economies, higher trade and capital account openess and lower inflation are more likely to discuss spillovers in their Article IV staff reports.