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001442554 001__ 1442554
001442554 003__ OCoLC
001442554 005__ 20230310003424.0
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001442554 019__ $$a1280604423$$a1281136930$$a1281968735$$a1283846954
001442554 020__ $$a9781484274347$$q(electronic bk.)
001442554 020__ $$a1484274342$$q(electronic bk.)
001442554 020__ $$z1484274334
001442554 020__ $$z9781484274330
001442554 0247_ $$a10.1007/978-1-4842-7434-7$$2doi
001442554 035__ $$aSP(OCoLC)1281249650
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001442554 049__ $$aISEA
001442554 050_4 $$aHB139$$b.N65 2022
001442554 08204 $$a330.015195$$223
001442554 1001_ $$aNokeri, Tshepo Chris,$$eauthor.
001442554 24510 $$aEconometrics and data science :$$bapply data science techniques to model complex problems and implement solutions for economic problems /$$cTshepo Chris Nokeri.
001442554 264_1 $$aNew York :$$bApress,$$c[2022]
001442554 300__ $$a1 online resource
001442554 336__ $$atext$$btxt$$2rdacontent
001442554 337__ $$acomputer$$bc$$2rdamedia
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001442554 5050_ $$aChapter 1 Introduction to Econometrics -- Chapter 2 Univariate Consumption Study Applying Regression -- Chapter 3 Multivariate Consumption Study Applying Regression -- Chapter 4 Forecasting Growth -- Chapter 5 Classifying Economic Data Applying Logistic Regression -- Chapter 6 Finding Hidden Patterns in World Economy and Growth -- Chapter 7 Clustering GNI Per Capita on a Continental Level -- Chapter 8 Solving Economic Problems Applying Artificial Neural Networks -- Chapter 9 Inflation Simulation -- Chapter 10 Economic Causal Analysis Applying Structural Equation Modelling.
001442554 506__ $$aAccess limited to authorized users.
001442554 520__ $$aGet 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
001442554 588__ $$aDescription based on print version record.
001442554 650_0 $$aEconometrics.
001442554 650_0 $$aQuantitative research.
001442554 650_6 $$aÉconométrie.
001442554 650_6 $$aRecherche quantitative.
001442554 655_0 $$aElectronic books.
001442554 77608 $$iPrint version:$$z1484274334$$z9781484274330$$w(OCoLC)1264137950
001442554 77608 $$iPrint version:$$aNokeri, Tshepo Chris.$$tEconometrics and data science.$$dNew York : Apress, 2022$$z9781484274330$$w(OCoLC)1285694226
001442554 852__ $$bebk
001442554 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7434-7$$zOnline Access$$91397441.1
001442554 909CO $$ooai:library.usi.edu:1442554$$pGLOBAL_SET
001442554 980__ $$aBIB
001442554 980__ $$aEBOOK
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