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

Description

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 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 For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Introduction to Econometrics Econometrics Economic Design Understanding Statistics Machine Learning Modeling Deep Learning Modeling Structural Equation Modeling Macroeconomic Data Sources Context of the Book Practical Implications Chapter 2: Univariate Consumption Study Applying Regression Context of This Chapter Theoretical Framework Lending Interest Rate Final Consumption Expenditure (in Current U.S. Dollars) The Normality Assumption Normality Detection Descriptive Statistics Covariance Analysis Correlation Analysis Ordinary Least-Squares Regression Model Development Using Statsmodels Ordinary Least-Squares Regression Model Development Using Scikit-Learn Cross-Validation Predictions Estimating Intercept and Coefficients Residual Analysis Other Ordinary Least-Squares Regression Model Performance Metrics Ordinary Least-Squares Regression Model Learning Curve Conclusion Chapter 3: Multivariate Consumption Study Applying Regression Context of This Chapter Social Contributions (Current LCU) Lending Interest Rate GDP Growth (Annual Percentage) Final Consumption Expenditure Theoretical Framework Descriptive Statistics Covariance Analysis Correlation Analysis Correlation Severity Detection Dimension Reduction Ordinary Least-Squares Regression Model Development Using Statsmodels Residual Analysis Residual Autocorrelation Ordinary Least-Squares Regression Model Development Using Scikit-Learn Cross-Validation Hyperparameter Optimization Residual Analysis Ordinary Least-Squares Regression Model Learning Curve Conclusion Chapter 4: Forecasting Growth Descriptive Statistics Stationarity Detection Random White Noise Detection Autocorrelation Detection Different Univariate Time Series Models The Autoregressive Integrated Moving Average The Seasonal Autoregressive Integrated Moving Average Model The Additive Model Additive Model Development Additive Model Forecast Seasonal Decomposition Conclusion Chapter 5: Classifying Economic Data Applying Logistic Regression Context of This Chapter Theoretical Framework Urban Population GNI per Capita, Atlas Method GDP Growth Life Expectancy at Birth, Total (in Years) Descriptive Statistics Covariance Analysis Correlation Analysis Correlation Severity Detection Dimension Reduction Making a Continuous Variable a Binary Logistic Regression Model Development Using Scikit-Learn Logistic Regression Confusion Matrix Logistic Regression Confusion Matrix Interpretation Logistic Regression Classification Report Logistic Regression ROC Curve Logistic Regression Precision-Recall Curve Logistic Regression Learning Curve Conclusion Chapter 6: Finding Hidden Patterns in World Economy and Growth Applying the Hidden Markov Model Descriptive Statistics Gaussian Mixture Model Development Representing Hidden States Graphically Order Hidden States Conclusion Chapter 7: Clustering GNI Per Capita on a Continental Level Context of This Chapter Descriptive Statistics Dimension Reduction Cluster Number Detection K-Means Model Development Predictions Cluster Centers Detection Cluster Results Analysis K-Means Model Evaluation The Silhouette Methods Conclusion Chapter 8: Solving Economic Problems Applying Artificial Neural Networks Context of This Chapter Theoretical Framework Restricted Boltzmann Machine Classifier Restricted Boltzmann Machine Classifier Development Restricted Boltzmann Machine Confusion Matrix Restricted Boltzmann Machine Classification Report Restricted Boltzmann Machine Classifier ROC Curve Restricted Boltzmann Machine Classifier Precision-Recall Curve Restricted Boltzmann Machine Classifier Learning Curve Multilayer Perceptron (MLP) Classifier Multilayer Perceptron (MLP) Classifier Model Development Multilayer Perceptron Classification Report Multilayer Perceptron ROC Curve Multilayer Perceptron Classifier Precision-Recall Curve Multilayer Perceptron Classifier Learning Curve Artificial Neural Network Prototyping Using Keras Artificial Neural Network Structuring Network Wrapping Keras Classifier Confusion Matrix Keras Classification Report Keras Classifier ROC Curve Keras Classifier Precision-Recall Curve Training Loss and Cross-Validation Loss Across Epochs Training Loss and Cross-Validation Loss Accuracy Across Epochs Conclusion Chapter 9: Inflation Simulation Understanding Simulation Context of This Chapter Descriptive Statistics Monte Carlo Simulation Model Development Simulation Results Simulation Distribution Chapter 10: Economic Causal Analysis Applying Structural Equation Modeling Framing Structural Relationships Context of This Chapter Theoretical Framework Final Consumption Expenditure Inflation and Consumer Prices Life Expectancy in Sweden GDP Per Capita Growth Covariance Analysis Correlation Analysis Correlation Severity Analysis Structural Equation Model Estimation Structural Equation Model Development Structural Equation Model Information Structural Equation Model Inspection Report Indices Visualize Structural Relationships Conclusion Index