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Econometrics and Data Science: Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems de Tshepo Chris Nokeri

Econometrics and Data Science: Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems

Autor Tshepo Chris Nokeri
en Limba Engleză Paperback – 27 oct 2021
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
 
Who This Book Is For

Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

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Specificații

ISBN-13: 9781484274330
ISBN-10: 1484274334
Pagini: 180
Ilustrații: XVIII, 228 p. 107 illus.
Dimensiuni: 178 x 254 x 18 mm
Greutate: 0.44 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

Chapter 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.

Notă biografică

Tshepo Chris Nokeri harnesses big data, advanced analytics, and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor’s degree in information management. He then graduated with an honors degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. He was unanimously awarded the Oxford University Press Prize. He has authored two Apress books: Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning, and Implementing Machine Learning for Finance: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios.

Textul de pe ultima copertă

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
 

Caracteristici

Simplifies the economic research process from data acquisition to model development and performance evaluation Introduces the use of mediating variables and ways of creating models that study multiple response variables Combines traditional methods and covers artificial neural networks and structural equation modeling