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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 57 items for :

  • Type: Journal Issue x
  • Mathematical and Quantitative Methods x
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Cover Selected Issues Papers
Estimating Fiscal Multiplier for Qatar: Qatar
Ken Miyajima
Econometric results suggest that Qatar’s strong capital spending multiplier became less impactful as the stock of capital rose to a high level, likely as the marginal impact declined. This supports Qatar’s strategy to shifts the State’s role to an enabler of private sector-led growth, focusing on expenditure to support build human capital and implementation of broader reform guided by the Third National Development Strategy.
Keywords:
Capital spending; fiscal multiplier; GCC; Qatar; strategy to shift; Qatar's strategy; copyright page; Qatar country dummy; allocation decision; Fiscal multipliers; Current spending; Stocks; Global
Cover IMF Working Papers
Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning
Yang Liu
,
Ran Pan
, and
Rui Xu
Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap.
Keywords:
Core inflation; forecasting; machine learning models; LASSO; Japan; household inflation expectation; forecasting inflation; machine learning method; Annex I. machine learning; enhanced inflation; Inflation; Machine learning; Econometric models; Output gap
Cover IMF Staff Country Reports
Kingdom of Lesotho: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper delves into few applications of machine learning (ML), with a particular application to economic forecasts in Lesotho. Amid delayed and often revised gross domestic product data, this paper explores the potential of ML to provide real-time insights into growth and inflation trends, crucial for informed policymaking. By leveraging nontraditional data and employing a variety of ML models, the paper presents a comprehensive analysis of current economic activity, evaluates the accuracy of standard statistical measures, and forecasts future inflation trends. The findings underscore the efficacy of ML in reducing prediction errors and highlight the significant role of alternative data in circumventing the limitations posed by traditional economic indicators. This paper contributes to the broader debate on the application of advanced computational techniques in economic forecasting, offering valuable insights for policymakers in Lesotho and similar countries grappling with data constraints and the need for timely economic analysis.
Keywords:
E. inflation forecasting; GDP Nowcasting result; D. GDP evaluation; C. GDP Nowcasting; ensemble Nowcasting model; Machine learning; Inflation; Africa; Global
Cover IMF Working Papers
Reinforcement Learning from Experience Feedback: Application to Economic Policy
Tohid Atashbar
Learning from the past is critical for shaping the future, especially when it comes to economic policymaking. Building upon the current methods in the application of Reinforcement Learning (RL) to the large language models (LLMs), this paper introduces Reinforcement Learning from Experience Feedback (RLXF), a procedure that tunes LLMs based on lessons from past experiences. RLXF integrates historical experiences into LLM training in two key ways - by training reward models on historical data, and by using that knowledge to fine-tune the LLMs. As a case study, we applied RLXF to tune an LLM using the IMF's MONA database to generate historically-grounded policy suggestions. The results demonstrate RLXF's potential to equip generative AI with a nuanced perspective informed by previous experiences. Overall, it seems RLXF could enable more informed applications of LLMs for economic policy, but this approach is not without the potential risks and limitations of relying heavily on historical data, as it may perpetuate biases and outdated assumptions.
Keywords:
LLMs; GAI; RLHF; RLAIF; RLXF; experience feedback; RL model; applying RLXF; Reinforcement Learning from AI feedback; Reinforcement Learning method; Artificial intelligence; Economic classification
Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
Flora Lutz
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover IMF Working Papers
Political Fragility: Coups d’État and Their Drivers
Aliona Cebotari
,
Enrique Chueca-Montuenga
,
Yoro Diallo
,
Yunsheng Ma
,
Rima A Turk
,
Weining Xin
, and
Harold Zavarce
The paper explores the drivers of political fragility by focusing on coups d’état as symptomatic of such fragility. It uses event studies to identify factors that exhibit significantly different dynamics in the runup to coups, and machine learning to identify these stressors and more structural determinants of fragility—as well as their nonlinear interactions—that create an environment propitious to coups. The paper finds that the destabilization of a country’s economic, political or security environment—such as low growth, high inflation, weak external positions, political instability and conflict—set the stage for a higher likelihood of coups, with overlapping stressors amplifying each other. These stressors are more likely to lead to breakdowns in political systems when demographic pressures and underlying structural weaknesses (especially poverty, exclusion, and weak governance) are present or when policies are weaker, through complex interactions. Conversely, strengthened fundamentals and macropolicies have higher returns in structurally fragile environments in terms of staving off political breakdowns, suggesting that continued engagement by multilateral institutions and donors in fragile situations is likely to yield particularly high dividends. The model performs well in predicting coups out of sample, having predicted a high probability of most 2020-23 coups, including in the Sahel region.
Keywords:
Fragility; Drivers of Fragility; Coup d’État; Machine Learning; coup dynamics; event study result; coup probability; probability of coup; IMF working paper 24/34; Fiscal stance; Income; Sub-Saharan Africa; Middle East and Central Asia; Caribbean
Cover IMF Staff Country Reports
Qatar: 2023 Article IV Consultation-Press Release; and Staff Report
International Monetary Fund. Middle East and Central Asia Dept.
The 2023 Article IV Consultation highlights that Qatar’s decade-long efforts to diversify the economy culminated into the successful hosting of the 2022 FIFA World Cup. Banks are well capitalized, liquid, and profitable, with the capital adequacy ratio and return on equity at 19 and 14.6 percent, respectively, in the second quarter of 2023. Banks’ nonresident deposits fell by more than one-third from the recent peak, partially replaced by higher public sector domestic deposits, reducing vulnerabilities amid tight global financial conditions. Structural reforms continue to progress, including to enhance protection and mobility of expatriate labor, improve the business environment, promote public–private partnerships, and further attract private investment through the residency program and broadened foreign ownership provisions. The pension scheme has been expanded to more Qataris in the private sector to promote private sector employment. If downside risks materialize, Qatar has strong policy buffers to mitigate the negative impact. On the upside, accelerated reform efforts guided by Third National Development Strategy could further promote diversification and boost potential growth.
Keywords:
LNG production expansion; money market rate; financial asset; IMF staff calculation; labor market dynamics; policy buffer; Fiscal stance; Anti-money laundering and combating the financing of terrorism (AML/CFT); Public sector; Global; Middle East; North Africa; East Africa
Cover IMF Working Papers
Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
Khaled AlAjmi
,
Jose Deodoro
,
Mr. Ashraf Khan
, and
Kei Moriya
Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research.
Keywords:
central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics; ML approach; IMF central bank transparency code; IMF Central Bank Legislation Database; capital markets Department; approaches to analyzing pattern; CBLD data flow; Central bank autonomy; Central bank transparency; Global
Cover IMF Working Papers
Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls
Silvia Albrizio
,
Allan Dizioli
, and
Pedro Vitale Simon
Using a novel approach involving natural language processing (NLP) algorithms, we construct a new cross-country index of firms' inflation expectations from earnings call transcripts. Our index has a high correlation with existing survey-based measures of firms' inflation expectations, it is robust to external validation tests and is built using a new method that outperforms other NLP algorithms. In an application of our index to United States, we uncover some facts related to firm's inflation expectations. We show that higher expected inflation translates into future inflation. Going into the firms level dimension of our index, we show departures from a rational framework in firms' inflation expectations and that firms' attention to the central enhances monetary policy effectiveness.
Keywords:
rms' inflation expectations; Firms' earnings calls transcripts; Natural Language processing; GPT3.5; Monetary policy; inflation expectation; expectation index; monetary policy effectiveness; firms attention; expectations disagreement; Inflation; Artificial intelligence; Wages; Futures; Consumer price indexes; Global
Cover FinTech Notes
Generative Artificial Intelligence in Finance: Risk Considerations
Mr. Ghiath Shabsigh
and
El Bachir Boukherouaa
In recent years, technological advances and competitive pressures have fueled rapid adoption of artificial intelligence (AI) in the financial sector, and this adoption is set to accelerate with the recent emergence of generative AI (GenAI). GenAI is a significant leap forward in AI technology that enhances its utility for financial institutions that have been quick at adapting it to a broad range of applications. However, there are risks inherent in the AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. GenAI could aggravate some of these risks and bring about new types or risks as well, including for financial sector stability. This paper provides early insights into GenAI’s inherent risks and their potential impact on the financial sector.
Keywords:
artificial intelligence; embedded bias; cybersecurity; fintech; financial risks; financial sector policy and analysis; financial stability; generative; hallucination; machine learning; privacy; synthetic data; sample generative AI application; AI-machine learning; IMF Library; IMF Fintech Note 2023/006; generative artificial intelligence in finance; Financial sector; Global