The Utilization of Statistical Machine Learning on Causal Linkage Between Public Debt and Economic Growth PDF Download
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Author: Mohammed Awal Osman
Publisher:
ISBN:
Category : Debts, Public
Languages : en
Pages : 0
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Book Description
The rising magnitude of public debt has been a primary concern of any government and policymakers in alleviating their economy and encourage economic growth. The results of many empirical studies in Ghana have proven the causal linkage between public debt and economic growth using traditional statistical methods such as regression and times series. The linear regression model has the most popular choice for designing this type of relationship [see Owusu-61Nantwi and Erickson (2016)]. Linear models suffer some set back such as the absence of normality and other standard assumptions and some constrained where economic complexity is concerned. The main objective of this study is to find a suitable learning algorithm to model the causal linkage between economic growth (GDP growth) and its associated economic variables. This research used time series data from 1970 to 2019. The effectiveness of the models will be evaluated by several diagnostic and goodness of fit tests and cross validation. We have considered three different types of models, the supervised multiple linear regression model, the semi-supervised diagnostic-robust regression model and unsupervised principal component regression model. Numerical results obtained from both model fitting and cross validation show that the diagnostic-robust regression performs best followed by the principal component regression and multiple linear regression.
Author: Mohammed Awal Osman
Publisher:
ISBN:
Category : Debts, Public
Languages : en
Pages : 0
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Book Description
The rising magnitude of public debt has been a primary concern of any government and policymakers in alleviating their economy and encourage economic growth. The results of many empirical studies in Ghana have proven the causal linkage between public debt and economic growth using traditional statistical methods such as regression and times series. The linear regression model has the most popular choice for designing this type of relationship [see Owusu-61Nantwi and Erickson (2016)]. Linear models suffer some set back such as the absence of normality and other standard assumptions and some constrained where economic complexity is concerned. The main objective of this study is to find a suitable learning algorithm to model the causal linkage between economic growth (GDP growth) and its associated economic variables. This research used time series data from 1970 to 2019. The effectiveness of the models will be evaluated by several diagnostic and goodness of fit tests and cross validation. We have considered three different types of models, the supervised multiple linear regression model, the semi-supervised diagnostic-robust regression model and unsupervised principal component regression model. Numerical results obtained from both model fitting and cross validation show that the diagnostic-robust regression performs best followed by the principal component regression and multiple linear regression.
Author: Mr.Andrew J Tiffin
Publisher: International Monetary Fund
ISBN: 1513518305
Category : Computers
Languages : en
Pages : 30
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Book Description
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.
Author: El Bachir Boukherouaa
Publisher: International Monetary Fund
ISBN: 1589063953
Category : Business & Economics
Languages : en
Pages : 35
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Book Description
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author: Atin Basuchoudhary
Publisher: Springer
ISBN: 3319690140
Category : Business & Economics
Languages : en
Pages : 97
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Book Description
This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.
Author: Sergio Consoli
Publisher: Springer Nature
ISBN: 3030668916
Category : Application software
Languages : en
Pages : 357
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Book Description
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Author: Cheng Few Lee
Publisher: World Scientific
ISBN: 9811202400
Category : Business & Economics
Languages : en
Pages : 5053
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Book Description
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Author: International Monetary Fund
Publisher: International Monetary Fund
ISBN: 1498344658
Category : Business & Economics
Languages : en
Pages : 257
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Book Description
This paper explores how fiscal policy can affect medium- to long-term growth. It identifies the main channels through which fiscal policy can influence growth and distills practical lessons for policymakers. The particular mix of policy measures, however, will depend on country-specific conditions, capacities, and preferences. The paper draws on the Fund’s extensive technical assistance on fiscal reforms as well as several analytical studies, including a novel approach for country studies, a statistical analysis of growth accelerations following fiscal reforms, and simulations of an endogenous growth model.
Author: John R. Vacca
Publisher: Elsevier
ISBN: 0128191317
Category : Social Science
Languages : en
Pages : 642
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Book Description
Smart Cities Policies and Financing: Approaches and Solutions is the definitive professional reference for harnessing the full potential of policy making and financial planning in smart cities. It covers the effective tools for capturing the dynamic relations between people, policies, financing, and environments, and where they are most often useful and effective for all relevant stakeholders. The book examines the key role of science, technology, and innovation (STI) - especially in information and communications technologies - in the design, development, and management of smart cities policies and financing. It identifies the problems and offers practical solutions in implementation of smart infrastructure policies and financing. Smart Cities Policies and Financing is also about how the implementation of smart infrastructure projects (related to the challenges of the lack of financing and the application of suitable policies) underlines the key roles of science, technology and innovation (STI) communities in addressing these challenges and provides key policies and financing that will help guide the design and development of smart cities. Brings together experts from academia, government and industry to offer state-of- the-art solutions for improving the lives of billions of people in cities around the globe Creates awareness among governments of the various policy tools available, such as output-based contracting, public-private partnerships, procurement policies, long-term contracting, and targeted research funds in order to promote smart infrastructure implementation, and encouraging the use of such tools to shape markets for smart infrastructure and correct market failures Ensures the insclusiveness of smart city projects by adequately addressing the special needs of marginalized sections of society including the elderly, persons with disabilities, and inhabitants of informal settlements and informal sectors Ensures gender considerations in the design of smart cities and infrastructure through the use of data generated by smart systems to make cities safer and more responsive to the needs of women Demonstrate practical implementation through real-life case studies Enhances reader comprehension using learning aids such as hands-on exercises, checklists, chapter summaries, review questions, and an extensive appendix of additional resources
Author: M. Ayhan Kose
Publisher: World Bank Publications
ISBN: 1464815453
Category : Business & Economics
Languages : en
Pages : 403
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Book Description
The global economy has experienced four waves of rapid debt accumulation over the past 50 years. The first three debt waves ended with financial crises in many emerging market and developing economies. During the current wave, which started in 2010, the increase in debt in these economies has already been larger, faster, and broader-based than in the previous three waves. Current low interest rates mitigate some of the risks associated with high debt. However, emerging market and developing economies are also confronted by weak growth prospects, mounting vulnerabilities, and elevated global risks. A menu of policy options is available to reduce the likelihood that the current debt wave will end in crisis and, if crises do take place, will alleviate their impact.
Author: Mizuho Kida
Publisher: International Monetary Fund
ISBN: 1513582437
Category : Business & Economics
Languages : en
Pages : 37
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Book Description
The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.
