Econometrics and data science : apply data science techniques to model complex problems and implement solutions for economic problems
著者
書誌事項
Econometrics and data science : apply data science techniques to model complex problems and implement solutions for economic problems
Apress, c2022
- : pbk
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注記
Includes index
内容説明・目次
内容説明
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
目次
Chapter 1 Introduction to EconometricsThis is the preliminary chapter of the book. It covers the application of data science practices in econometrics.Sub-topics Econometrics Economic design Comprehending statistics Learning modeling Deep learning modeling Structural equation modeling Macroeconomic data source Context of the book Practical implications
Chapter 2 Univariate Consumption Study Applying RegressionThis chapter introduces a simple linear regression model known as the ordinary least-square model. It applies the model to determine whether changes in lending interest rate (%) influence changes in final consumption expenditure (current US$) in the USA. It contains ways of conducting covariance analysis, correlation analysis, model development, cross-validation, hyperparameter optimization, and model evaluation.Sub-topics Context of this chapter Theoretical frameworka) lending interest rate (%)b) Final consumption expenditure (current US$) The normality assumptiona) normality detection Descriptive statistics Covariance analysis Correlation analysis The Pearson correlation method Ordinary least squares model development using statsmodels Ordinary least squares model development using SciKit-Learna) Cross-validationb) Predictionsc) Intercept and coefficients estimationd) Residualse) Other ordinary least-square regression performance metricsf) Learning curve Conclusion
Chapter 3 Multivariate Consumption Study Applying RegressionThe preceding chapter carefully covered simple linear regression-a model for predicting continuous response variables using a predictor variable. There are cases where there is over one predictor variable. This chapter presents ways of properly fitting multiple variables into a regression equation. It applies the ordinary least-square model to examine whether changes in social contributions (current LCU), lending interest rate (%), and GDP growth (annual %) influence changes in final consumption expenditure (current US$). First, it applies the Pearson correlation method to study the correlation among the variables, and then it implements the Eigen matrix to determine the severity among variables.Sub-topics Context of This Chaptera. Social contributions (current LCU)b. Lending interest rate (%)c. GDP growth (Annual %)d. Final consumption expenditure (Current US$) Theoretical framework Descriptive statistics Covariance analysis Correlation analysis Correlation severity detection Dimension reduction Ordinary least squares model development using statsmodelsa. Residual analysis Residual autocorrelation Ordinary least squares model development using sciKit-learn cross-validationa. Hyperparameter optimizationb. Residual analysis Residual autocorrelationa. Learning curve
Chapter 4 Forecasting GrowthThis chapter covers a time series analysis model recognized as the additive model to forecast future instances of future GDP growth (annual %) in the U.S. Before implementing the model, it first discusses time series analysis assumptions, thereafter it covers tests for stationarity, white noise, and autocorrelation and different models for time series analysis.Sub-topics Descriptive statistics Stationarity detection Random white noise detection Autocorrelation detection Different univariate time series modelsa. The autoregressive integrated moving averageb. The seasonal autoregressive integrated moving average modelc. The additive model Additive model developmenta. Additive model forecast Seasonal decomposition
Chapter 5 Classifying Economic Data Applying Logistic RegressionThis chapter introduces a binary classification method recognized as logistic regression. To begin with, it covers descriptive analysis, covariance analysis, correlation analysis, correlation severity analysis, and dimension reduction. Following that, it exposes a viable way of binarizing a continuous variable. Next, it employs the sigmoid function to operate an urban population, GNI per Capita, Atlas Method (Current US$), GDP growth (annual %), then predict decreasing and increasing life expectancy at birth, total (years) in the USA. Last, it hands out ways of analyzing model performance using the confusion matrix, classification report, ROC curve, Precision-Recall curve, and learning curve.Sub-topics The multicollinearity problem Context of this chapter Theoretical frameworka. Urban populationb. GNI per capita, Atlas method (current US$)c. GDP growth (Annual %)d. Life expectancy at birth, total (years) Outlier detection Descriptive statistics Covariance analysis Correlation analysis Correlation severity detection Binarize a continuous variable Dimension reduction Logistic regression model performance evaluationa. Confusion matrixb. Classification reportc. ROC curved. Precision-recall curvee. Learning curve Conclusion
Chapter 6 Finding Hidden Patterns in World Economy and GrowthThis chapter introduces decision-making using the Hidden Markov Model. It applies the Gaussian Mixture model to identify hidden patterns in the world economy and growth to forecast futurepatterns.Sub-topics Application of the hidden Markov Model Descriptive statistics Gaussian mixture model development Graphically representing hidden states Order hidden states
Chapter 7 Clustering GNI Per Capita on a Continental LevelThis chapter covers an unsupervised machine learning model for clustering known as the K-Mean. At the outset, it covers covariance analysis, correlation analysis, and dimension reduction on GNI per capita data of African countries. Next, it refers to an elbow curve to determine the number of clusters to include in the model. Last, it examines predicted labels and applies the Silhouette method to analyze the model's performance.Sub-topics Context of this chapter Descriptive statistics Dimension reduction Cluster number detection Cluster results analysis K-Means model developmenta. Predictionsb. Cluster centres detection K-Means model evaluationa. The Silhouette method
Chapter 8 Solving Economic Problems Applying Artificial Neural NetworksThis chapter provides a high-level overview of deep learning. First, it covers a basic artificial neuralnetwork recognized as the Restricted Boltzmann Machine. In addition, it discusses the shortcomings of shallow networks and how modern artificial neural networks overcome them. Next, it encloses the Multilayer Perceptron and techniques for developing more complex artificial neural networks.Sub-topics Context of this chapter Theoretical framework Restricted Boltzmann machinea. Restricted Boltzmann machine classifier developmentb. Model evaluation Confusion matrix Classification report ROC curve Precision-recall curve Learning curve Multilayer Perceptrona. Multilayer perceptron model developmentb. Model evaluation Confusion matrix Classification report ROC curve Precision-recall curve Learning curve Building neural networks with Kerasa. Network architectureb. Network wrappingc. Network trainingd. Model development Confusion matrix Classification report ROC curve Precision-recall curve Training loss and cross-validation loss across epochs Training loss and cross-validation loss accuracy across epochs
Chapter 9 Inflation SimulationThis chapter examines the impact of different scenarios of central government debt in Great Britain using the Monte Carlo simulation method. In particular, it employs this method to determine the probability of a change in the country's central government debt across multiple trials.Sub-topics Understanding simulation Descriptive statistics Monte Carlo Simulation Model Developmenta. Simulation resultsb. Simulation distribution
Chapter 10 Economic Causal Analysis Applying Structural Equation ModellingThis chapter introduces a model for determining causal relationships between variables known as structural equation modeling. To begin with, it covers how one can frame a structural relationship, introduce a mediating variable to a structural equation, and develop a structural equation model. To conclude, it explores the technique of presenting model information, inspection, and reporting indices, and visualizing a structural relationship with a mediator.Sub-topics Framing structural relationships Context of this chaptera. Final consumption expenditure (current US$)b. Inflation, consumer prices (annual %)c. Life expectancy at birth, total (years)d. GDP growth (Annual %) Covariance analysis Correlation analysis Correlation severity detection Structural equation model estimation Structural equation model development Structural equation model information Structural equation model inspection Report indices Visualize structural relationship
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