Introduction to Econometrics
March 2008, ©2008
Chapter 1: An Overview of Econometrics.
1.1 The Importance of Econometrics.
1.2 Types of Economic Data.
1.3 Working with Data: Graphical Methods.
1.4 Working with Data: Descriptive Statistics and Correlation.
1.5 Chapter Summary.
Chapter 2: A Non-technical Introduction to Regression.
2.2 The Simple Regression Model.
2.3 The Multiple Regression Model.
2.4 Chapter Summary.
Chapter 3: The Econometrics of the Simple Regression Model.
3.2 A Review of Basic Concepts in Probability in the Context of the Regression Model.
3.3 The Classical Assumptions for the Regression Model.
3.4 Properties of the Ordinary Least Squares Estimator of β.
3.5 Deriving a Confidence Interval for β.
3.6 Hypothesis Tests about β.
3.7 Modifications to Statistical Procedures when σ2 is Unknown.
3.8 Chapter Summary.
Appendix 1: Proof of the Gauss-Markov theorem.
Appendix 2: Using a Asymototic Theory in the Simple Regression Model.
Chapter 4: The Econometrics of the Multiple Regression Model.
4.2 Basic Results for the Multiple Regression Model.
4.3 Issues Relating to the Choice of Explanatory Variables.
4.4 Hypothesis Testing in the Multiple Regression Model.
4.5 Choice of Functional Form in the Multiple Regression Model.
4.6 Chapter Summary.
Appendix: Wald and Lagrange multiplier tests.
Chapter 5: The Multiple Regression Model: Freeing up Classical Assumptions.
5.2 Basic Theoretical Results.
5.4 The Regression Model with Autocorrelated Errors.
5.5 The Instrumental Variables Estimator.
5.6 Chapter Summary.
Appendix: Asymptotic Results for the OLS and Instrumental variables Estimators.
Chapter 6: Univariate Time Series Analysis.
6.2 Time Series Notation.
6.3 Trends in Time Series Variables.
6.4 The Autocorrelation Function.
6.5 The Autoregressive Model.
6.6 Defining Stationarity.
6.7 Modelling Volatility.
6.8 Chapter Summary.
Appendix: MA and ARMA Models.
Chapter 7: Regression with Time Series Variables.
7.2 Time Series Regression when X and Y are Stationary.
7.3 Time Series Regression When Y and X have Unit Roots.
7.4 Time Series Regression when Y and X have Unit Roots but are NOT Cointegrated.
7.5 Granger Causality.
7.6 Vector Autoregressions.
7.7 Chapter Summary.
Appendix: The Theory of Forecasting.
Chapter 8: Models for Panel Data.
8.2 The Pooled Model.
8.3 Individual Effects Models.
8.4 Chapter Summary.
Chapter 9: Qualitative Choice and Limited Dependent Variable Models.
9.2 Qualitative Choice Models.
9.3 Limited Dependent Variable Models.
9.4 Chapter Summary.
Chapter 10: Bayesian Econometrics.
10.1 An Overview of Bayesian Econometrics.
10.2 The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.
10.3 Chapter Summary.
Appendix: Bayesian Analysis of the Simple Regression Model with Unknown Variance.
Appendix A; Mathematical Basics.
Appendix B: Probability Basics.
Appendix C: Basic Concepts in Asymptotic Theory.
Appendix D: Writing an Empirical Project.
- Addresses problems that many students face with existing textbooks by providing a balanced introduction to the statistical and probability theory that underlies econometrics and also to the tools that are required to do practical data work.
- Places a greater emphasis on econometrics models e.g. ARMA models or VARs rather than the methods used to analyze those models.
- Provides a bridge between introductory and more advanced undergraduate courses in econometrics.
- Caters for students of differing mathematical and statistical abilities.
- Offers a good training in doing the basics of empirical work.
- Extensive empirical examples, problem sets and computer materials will be provided on an accompanying website.
Chaps 1 & 2 provide non-technical background to basic tools and allow students to start empirical work
Chap 3 onwards – theoretical econometrics begins
Includes a wide range of models used by economists e.g. ARMA, VAR, logit and probit