Description based upon print version of record.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- List of figures -- Preface -- 1. Introduction -- 1.1. Introduction -- 1.2. The motivation for writing this book -- 1.3. The focus of this book -- 1.4. The scope and structure of the book -- 2. Methodology Review -- 2.1. Introduction -- 2.2. The creation of econometrics -- 2.3. Social physics -- 2.3.1. Deductive and inductive -- 2.3.2. Statistical induction -- 2.3.3. Motivations of creating social physics -- 2.3.4. 'Mathematic games' vs. science for econometrics

2.4. Core ideas of science in econometrics -- 2.4.1. Error assessment and probability application -- 2.4.2. Stochastic and deterministic -- 2.4.3. The 'Error' concept in modern statistics -- 2.5. Probability revolution in econometrics -- 2.5.1. Koopmans and Haavelmo's important contributions -- 2.5.2. Various estimation methods as an approximation -- 2.5.3. Theoretical assumption tests -- 2.5.4. Probability approach in time series analysis -- 2.6. 'Discovering' from data -- 2.6.1. The econometric model approach -- 2.6.2. Box-Jenkins' ARIMA approach -- 2.7. From static to dynamic modelling

2.7.1. Structural econometric time series approach -- 2.7.2. Dynamic linear models -- 2.7.3. The Bayesian and non-Bayesian approaches -- 2.8. Debates of statistical significance in the 21st century -- 2.8.1. Debates on p-values -- 2.8.2. The reasons for stopping using p-values -- 2.8.3. Disagreed opinions -- 2.9. Summary and conclusions -- 3. Methodology of Simple Regression Analysis -- 3.1. Introduction -- 3.2. Simple regression analysis -- 3.2.1. Population regression functions (PRF) -- 3.2.2. Sample regression functions (SRF) -- 3.2.3. The relationship between PRF and SRF

3.3. Estimation methods for SRF coefficients -- 3.3.1. Population mean and sample mean -- 3.3.2. Least square techniques -- 3.4. Standard statistical assumptions -- 3.4.1. Statistical assumptions of the PRF estimation -- 3.4.2. The normal distribution in the regression model -- 3.5. Best linear unbiased estimators (BLUE) -- 3.5.1. The unbiased estimate -- 3.5.2. Efficiency -- 3.5.3. The precision of regression coefficients -- 3.6. Accuracy assessments and model valuations -- 3.6.1. The residual standard error (RSE) -- 3.6.2. 'Goodness of fit' -- R2 statistic

3.7. The null hypothesis statistical testing -- 3.7.1. The confidence interval estimation -- 3.7.2. The statistical significant test -- 3.8. The crisis of statistical science in p-values -- 3.8.1. Misinterpretation of the p-values -- 3.8.2. Misuse of the p-values -- 3.8.3. Arbitrary threshold value of p < 0.05 -- 3.8.4. Practical alternatives to p-values -- 3.9. The simulation approach -- 3.10. Summary and conclusions -- 4. Methodology of Multiple Regression Analysis -- 4.1. Introduction -- 4.2. Methodology of multiple linear regressions

4.3. Least square estimation in multiple regression models.

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