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More data has been produced in the 21st century than all of human history combined. Yet, are we making better decisions than yesterday's generation? Do we believe that poor decisions tend to result from the absence of data? The existence of an overwhelming amount of data has affected how we make decisions, but it has not necessarily improved how we make decisions. To make better decisions, people need good judgment based on data literacy--the ability to extract meaning from data. This book opens with cautionary tales of what can happen when too much attention is spent on acquiring more data instead of understanding how to best use the existing data. Including data in the decision-making process can bring considerable clarity in answering our questions. Nevertheless, the book explores many examples in business and politics in which too much data resulted in bad decisions. Human beings can become distracted, overwhelmed, and even confused. Data is not generated in a vacuum. The book's primary thesis is that people who possess data literacy will understand the environment and incentives behind the data. With this understanding in place, good decisions will follow. More Judgment Than Data introduces the principles of data literacy. Readers will learn what questions to ask, what data to pay attention to, and what pitfalls to avoid. As an application, a chapter expounds upon these data literacy principles in a COVID-19 era. Readers will not only learn how to make better decisions, they will become less vulnerable to others who manipulate data for misleading purposes.

Quantitative methods (economics) --- Information systems --- econometrie --- gegevensanalyse

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Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models .

Quantitative methods (economics) --- Programming --- Python (informatica) --- econometrie

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Quantitative methods (economics) --- Programming --- Python (informatica) --- econometrie --- Econometrics. --- Quantitative research.

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This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. .

Macroeconomics --- Quantitative methods (economics) --- Programming --- programmeren (informatica) --- macro-economie --- econometrie

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This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

Quantitative methods (economics) --- Production management --- risk management --- econometrie