Econometrics and Data Science

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