Econometrics and Data Science : Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems
1st ed.
Book Details
Format
Paperback / Softback
ISBN-10
1484274334
ISBN-13
9781484274330
Edition
1st ed.
Publisher
APress
Imprint
APress
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Oct 27th, 2021
Print length
228 Pages
Weight
482 grams
Dimensions
17.70 x 25.40 x 1.80 cms
Product Classification:
EconometricsProbability & statisticsDatabasesMachine learning
Ksh 5,400.00
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Intermediate-Advanced user level
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
Get Econometrics and Data Science by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by APress and it has pages.
