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

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. -- Provided by publisher Table of Contents 5 About the Author 11 About the Technical Reviewer 12 Acknowledgments 13 Introduction 14 Chapter 1: Introduction to Econometrics 16 Econometrics 16 Economic Design 17 Understanding Statistics 18 Machine Learning Modeling 18 Deep Learning Modeling 19 Structural Equation Modeling 21 Macroeconomic Data Sources 21 Context of the Book 26 Practical Implications 27 Chapter 2: Univariate Consumption Study Applying Regression 28 Context of This Chapter 30 Theoretical Framework 30 Lending Interest Rate 31 Final Consumption Expenditure (in Current U.S. Dollars) 32 The Normality Assumption 34 Normality Detection 34 Descriptive Statistics 35 Covariance Analysis 39 Correlation Analysis 40 Ordinary Least-Squares Regression Model Development Using Statsmodels 43 Ordinary Least-Squares Regression Model Development Using Scikit-Learn 45 Cross-Validation 46 Predictions 47 Estimating Intercept and Coefficients 49 Residual Analysis 52 Other Ordinary Least-Squares Regression Model Performance Metrics 56 Ordinary Least-Squares Regression Model Learning Curve 57 Conclusion 59 Chapter 3: Multivariate Consumption Study Applying Regression 60 Context of This Chapter 61 Social Contributions (Current LCU) 61 Lending Interest Rate 62 GDP Growth (Annual Percentage) 64 Final Consumption Expenditure 65 Theoretical Framework 67 Descriptive Statistics 69 Covariance Analysis 75 Correlation Analysis 76 Correlation Severity Detection 78 Dimension Reduction 80 Ordinary Least-Squares Regression Model Development Using Statsmodels 83 Residual Analysis 86 Residual Autocorrelation 87 Ordinary Least-Squares Regression Model Development Using Scikit-Learn 88 Cross-Validation 88 Hyperparameter Optimization 89 Residual Analysis 90 Ordinary Least-Squares Regression Model Learning Curve 95 Conclusion 97 Chapter 4: Forecasting Growth 98 Descriptive Statistics 100 Stationarity Detection 103 Random White Noise Detection 104 Autocorrelation Detection 105 Different Univariate Time Series Models 108 The Autoregressive Integrated Moving Average 108 The Seasonal Autoregressive Integrated Moving Average Model 108 The Additive Model 108 Additive Model Development 109 Additive Model Forecast 110 Seasonal Decomposition 111 Conclusion 111 Chapter 5: Classifying Economic Data Applying Logistic Regression 112 Context of This Chapter 113 Theoretical Framework 114 Urban Population 114 GNI per Capita, Atlas Method 116 GDP Growth 117 Life Expectancy at Birth, Total (in Years) 118 Descriptive Statistics 121 Covariance Analysis 127 Correlation Analysis 128 Correlation Severity Detection 131 Dimension Reduction 131 Making a Continuous Variable a Binary 134 Logistic Regression Model Development Using Scikit-Learn 135 Logistic Regression Confusion Matrix 136 Logistic Regression Confusion Matrix Interpretation 137 Logistic Regression Classification Report 138 Logistic Regression ROC Curve 139 Logistic Regression Precision-Recall Curve 140 Logistic Regression Learning Curve 142 Conclusion 143 Chapter 6: Finding Hidden Patterns in World Economy and Growth 144 Applying the Hidden Markov Model 145 Descriptive Statistics 146 Gaussian Mixture Model Development 150 Representing Hidden States Graphically 151 Order Hidden States 156 Conclusion 156 Chapter 7: Clustering GNI Per Capita on a Continental Level 157 Context of This Chapter 158 Descriptive Statistics 161 Dimension Reduction 165 Cluster Number Detection 166 K-Means Model Development 168 Predictions 168 Cluster Centers Detection 169 Cluster Results Analysis 171 K-Means Model Evaluation 173 The Silhouette Methods 173 Conclusion 173 Chapter 8: Solving Economic Problems Applying Artificial Neural Networks 174 Context of This Chapter 175 Theoretical Framework 175 Restricted Boltzmann Machine Classifier 177 Restricted Boltzmann Machine Classifier Development 178 Restricted Boltzmann Machine Confusion Matrix 178 Restricted Boltzmann Machine Classification Report 179 Restricted Boltzmann Machine Classifier ROC Curve 180 Restricted Boltzmann Machine Classifier Precision-Recall Curve 181 Restricted Boltzmann Machine Classifier Learning Curve 183 Multilayer Perceptron (MLP) Classifier 184 Multilayer Perceptron (MLP) Classifier Model Development 185 Multilayer Perceptron Classification Report 187 Multilayer Perceptron ROC Curve 187 Multilayer Perceptron Classifier Precision-Recall Curve 189 Multilayer Perceptron Classifier Learning Curve 190 Artificial Neural Network Prototyping Using Keras 192 Artificial Neural Network Structuring 192 Network Wrapping 193 Keras Classifier Confusion Matrix 194 Keras Classification Report 195 Keras Classifier ROC Curve 195 Keras Classifier Precision-Recall Curve 197 Training Loss and Cross-Validation Loss Across Epochs 198 Training Loss and Cross-Validation Loss Accuracy Across Epochs 200 Conclusion 201 Chapter 9: Inflation Simulation 202 Understanding Simulation 202 Context of This Chapter 203 Descriptive Statistics 204 Monte Carlo Simulation Model Development 207 Simulation Results 208 Simulation Distribution 209 Chapter 10: Economic Causal Analysis Applying Structural Equation Modeling 214 Framing Structural Relationships 215 Context of This Chapter 215 Theoretical Framework 217 Final Consumption Expenditure 217 Inflation and Consumer Prices 219 Life Expectancy in Sweden 220 GDP Per Capita Growth 221 Covariance Analysis 225 Correlation Analysis 226 Correlation Severity Analysis 229 Structural Equation Model Estimation 229 Structural Equation Model Development 230 Structural Equation Model Information 230 Structural Equation Model Inspection 233 Report Indices 234 Visualize Structural Relationships 236 Conclusion 237 Index 238