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

This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.

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This book bridges the gap between econometrics and data science techniques. It introduces a holistic approach to satisfactorily solving economic problems from a machine learning perspective. It begins by discussing the practical benefits of harnessing data science techniques in econometrics. It then clarifies the key concepts of variance, covariance, and correlation, and covers the most common linear regression model, called ordinary least-squares. It explains the techniques for testing assumptions through residual analysis, including other evaluation metrics (i.e., mean absolute error, mean squared error, root mean squared error, and R2). It also exhibits ways to correctly interpret your findings. Following that, it presents an approach to tackling series time data by implementing an alternative model to the dominant time series analysis models (i.e., ARIMA and SARIMA), called the additive model. That model typically adds non-linearity and smooth parameters.

The book also introduces ways to capture non-linearity in economic data by implementing the most prevalent binary classifier, called logistic regression, alongside metrics for evaluating the model (i.e., confusion matrix, classification report, ROC curve, and precision-recall curve). In addition, youll learn about a technique for identifying hidden states in economic data by implementing the Hidden Markov modeling technique, together with an approach for realizing mean and variance in each state. Youll also learn how to categorize countries grounded on similarities by implementing the most common cluster analysis model, called the K-Means model, which implements the Euclidean distance metric.

The book also covers the practical application of deep learning in econometrics by showing key artificial neural networks (i.e., restricted Boltzmann machine, multilayer perceptron, and deep belief networks), including ways of adding more complexity to networks by expanding hidden layers. Then, it familiarizes you with a method of replicating economic activities across multiple trials by implementing the Monte Carlo simulation technique. The books concludes by presenting a standard procedure for testing causal relationships among variables, including the mediating effects of other variables in those relationships, by implementing structural equation modeling (SEM).

This book targets beginners to intermediate economists, data scientists, and machine learning engineers who want to learn how to approach econometrics problems from a machine learning perspective using an array of Python libraries.

Writing a single-authored book is demanding, but I received firm support and active encouragement from my family and dear friends. Many heartfelt thanks to the Apress Publishing team for all their support throughout the writing and editing processes. Last, my humble thanks to all of you for reading this; I earnestly hope you find it helpful.