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 ForBeginning and intermediate data scientists
economists machine learning engineers statisticians and business executives
