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Economics --- Econometrics --- Methodology --- Économie politique --- Économétrie --- Méthodologie

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This project will have a stamped cover, therefore no back text can be used.

Macroeconomics --- Quantitative methods (economics) --- Finance --- macro-economie --- financiën --- econometrie

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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 LearnExamine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden statesBe familiar with practical applications of machine learning and deep learning in econometricsUnderstand theoretical framework and hypothesis development, and techniques for selecting appropriate modelsDevelop, 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 modelsRepresent and interpret data and models Who This Book Is ForBeginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

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

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This volume is the result of an Advances in Econometrics conference held in November of 2002 at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in 1980 and 1982 on robust variance-covariance estimation and quasi-maximum likelihood estimation. It contains 11 papers on a range of related topics including the estimation of possibly misspecified error component and fixed effects panel models, estimation and inference in possibly misspecified quantile regression models, quasi-maximum likelihood estimation of linear regression models with bounded and symmetric errors and quasi-maximum likelihood estimation of models with parameter dependencies between the mean vector and error variance-covariance matrix. Other topics include GMM, HAC, Heckit, asymmetric GARCH, Cross-Entropy, and multivariate deterministic trend estimation and testing under various possible misspecifications.

Mathematical statistics --- 330.115 --- 330.115 Econometrie --- Econometrie --- Econometric models --- Econometrics --- Economics, Mathematical --- Statistics --- Mathematical models --- Econometrics. --- Econometric models. --- Business & Economics

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This book presents a survey of the aspects of economic complexity, with a focus on foundational, interdisciplinary ideas. The long-awaited follow up to his 2011 volume Complex Evolutionary Dynamics in Urban-Regional and Ecologic-Economic Systems: From Catastrophe to Chaos and Beyond, this volume draws together the threads of Rosser’s earlier work on complexity theory and its wide applications in economics and an expanded list of related disciplines. The book begins with a full account of the broader categories of complexity in economics--dynamic, computational, hierarchical, and structural--before shifting to more detailed analysis. The next two chapters address problems associated with computational complexity, especially those of computability, and discuss the Godel Incompleteness Theorem with a focus on reflexivity. The middle chapters discuss the relationship between entropy, econophysics, evolution, and economic complexity, respectively, with applications in urban and regional dynamics, ecological economics, general equilibrium theory, as well as financial market dynamics. The final chapter works to bring together these themes into a broader framework and expose some of the limits concerning analysis of deeper foundational issues. With applications in all disciplines characterized by interconnected nonlinear adaptive systems, this book is appropriate for graduate students, professors and practitioners in economics and related disciplines such as regional science, mathematics, physics, biology, environmental sciences, philosophy, and psychology.

Quantitative methods (economics) --- Economics --- Recreation. Games. Sports. Corp. expression --- economie --- spellen --- econometrie --- Evolutionary economics.