Course Outline

I. Material Covered Prior to First Exam

• Introduction, Course Overview, Data Types, Marginal Densities, Moments of a Scalar Random Variable
  • Lecture Notes, Wooldridge: Chapter 1, Appendix B

• Bivariate, Joint and Conditional Distributions, Conditional Expectations, Independence, Iterated Expectations, First Two Moments of a Random Vector
  • Lecture Notes, Wooldridge: Chapter 1, Appendix B

• Unbiasedness, Notions of Convergence, Consistency, Convergence in Mean Square, Convergence in Probability, Law of Large Numbers, Central Limit Theorem, Sampling Distributions
  • Lecture Notes, Wooldridge Appendix C.1-C.3
  • PowerPoint Show: Law of Large Numbers and Central Limit Theorem
  • Powerpoint Show: Sampling Distributions

• Special Distributions, Introduction to the Simple Linear Regression Model, Least Squares Estimation
  • Wooldridge, Appendix B.5, Chapter 2.1-2.2
  • Powerpoint Show: Regression Introduction

• Estimated Residuals, Fitted Values, R-squared, Sampling Distribution of the OLS Estimator
  • Wooldridge, 2.2, 2.3, 2.5

II. Material Covered Before Second Exam

• Interpretation of Regression Coefficients Under Common Logarithmic Transformations:
  • Wooldridge, 2.4

• The General k-Variable Linear Regression Model: Set-up and Estimation Using Matrix Algebra
  • Wooldridge, Appendix E.1, E.2

• Assumptions of the Linear Model, Properties of the OLS Estimator: Unbiasedness, Consistency, Asymptotic Normality, Efficiency (Gauss-Markov Theorem)
  • Lecture Notes, Wooldridge, Appendix E.1, E.2, Chapter 3

• Short versus Long Regression and the Interpretation of Multiple Regression Coefficients
  • Lecture Notes

• Omitted Variable Bias
  • Wooldridge, Chapter 3.3

• Hypothesis Testing: Scalar (one and two-sided) tests, p-values, confidence intervals
  • Wooldridge, Chapter 4.1-4.3
  • Powerpoint Show: Confidence Intervals, p-values, scalar testing

• Joint Hypothesis Testing
  • Wooldridge, Chapter 4.4-4.6

• Dummy Variables and Interactions
  • Wooldridge, Chapter 7 (all but 7.5)

III. Material Covered Before Final Exam

• Heteroscedasticity
  • Wooldridge, 8.1,8.2,8.4, Lecture Notes

• Multicollinearity
  • Wooldridge, 3.4, Lecture Notes

• Mean-Independence Violations:
  • Measurement Error (in x), Wooldridge, 9.3, 15.4
  • Endogeneity of right-hand side variables, Wooldridge, 15.1-15.4
  • Simultaneity, Wooldridge, 16.1-16.4
  • Instrumental Variables Wooldridge, 15.1-15.4
  • Two Stage Least Squares Estimation Wooldridge, 15.1-15.4

• Models for Discrete Choice
  • Binary Choice Models
    • Linear Probability Model (LPM), Wooldridge, 7.5, 8.5
    • Probit Model, Wooldridge, 17.1
    • Logit Model, Wooldridge, 17.1
  • Models of Ordered Outcomes: The Ordered Probit Lecture Notes
  • Models of Censored Outcomes: The Tobit Model Wooldridge, 17.2

• Panel Data (Time Permitting)
  • Wooldridge, Chapters 13, 14