Introductory Econometrics is mainly a course on regression analysis, this is, a course about modelling the conditional expectation of a random variable by means of a linear function. Multiple regression analysis is the core of more advanced econometrics techniques; hence it is the natural starting point for an introductory course.

Part I: Introduction

1- THE NATURE OF ECONOMETRICS AND ECONOMIC DATA

1.1 What is econometrics?

1.2 Steps in empirical economic analysis.

1.3 The structure of economic data.

1.4 Causality and the notion of ceteris paribus in econometric analysis.

References: Wooldridge: Chapter 1, p.-1-19.

Part II: Regression Analysis with Cross-Sectional Data

2- THE SIMPLE REGRESSION MODEL

2.1 Definition of the simple regression model.

2.2 Deriving the Ordinary Least Squares (OLS) estimates.

2.3 Mechanics of OLS.

2.4 Units of measurement and functional form.

2.5 Expected values and variances of the OLS estimators.

2.6 Regression through the origin.

References: Wooldridge: Chapter 2, p.-21-67.

3- MULTIPLE REGRESSION ANALYSIS: ESTIMATION

3.1 Motivation for multiple regression

3.2 Mechanics and interpretation of Ordinary Least Squares (OLS).

3.3 The expected value of the OLS estimators.

3.4 The variance of the OLS estimators.

3.5 Efficiency of OLS: The Gauss-Markov Theorem.

References: Wooldridge: Chapter 3, p.-68-115.

4- MULTIPLE REGRESSION ANALYSIS: INFERENCE

4.1 Sampling distributions of the OLS estimators.

4.2 Testing hypothesis about a single population parameter: The t test.

4.3 Confidence intervals.

4.4 Testing hypothesis about a single linear combination of the parameters.

4.5 Testing multiple linear restrictions: The F test.

4.6 Reporting regression results

References: Wooldridge: Chapter 4, p.-116-165.

5- MULTIPLE REGRESSION ANALYSIS: OLS ASYMPTOTICS

5.1 Consistency.

5.2 Asymptotic normality and large sample inference.

5.3 Asymptotic efficiency of OLS.

References: Wooldridge: Chapter 5, p.-166-181.

6 MULTIPLE REGRESSION ANALYSIS: FURTHER ISSUES

6.1 Effects of data scaling on OLS statistics.

6.2 More on functional form.

6.3 More on goodness-of-fit and selection of regressors.

6.4 Prediction and residual analysis.

References: Wooldridge: Chapter 6, p.-182-217.

7- MULTIPLE REGRESSION ANALYSIS WITH QUALITATIVE INFORMATION: BINARY (OR DUMMY) VARIABLES

7.1 Describing qualitative information.

7.2 A single dummy independent variable.

7.3 Using dummy variables for multiple categories.

7.4 Interactions involving dummy variables.

7.5 A binary dependent variable: The linear probability model.

7.6 More on policy analysis and program evaluation.

References: Wooldridge: Chapter 7, p.-218-256.

8- HETEROSKEDASTICITY

8.1 Consequences of heteroskedasticity for OLS.

8.2 Heteroskedasticity-robust inference after OLS estimation.

8.3 Testing for heteroskedasticity.

8.4 Weighted least squares estimation (WLS).

8.5 The linear probability model revisited.

References: Wooldridge: Chapter 8, p.-257-288.