This handout does not provide any additional information about the exam. It only provides some tips for your last-minute revisions and highlights the what you may wish to look at as a final check before the exam.

  • The most important thing you should do is go through the review exercises (if you have not done so yet).

  • Make sure you are comfortable with answering these questions. As we mentioned throughout the term, the exam questions will look similar to the review exercises.

  • There are some topics that do not have review exercises. For such topics, you will either get multiple-choice questions or short-answer questions that ask for explanations.

Below is some information on the structure of the exam, followed by a list of all the contents and posted materials on the Moodle page.

1 Structure of the Exam

  • The exam will consist of a section with multiple choice questions and a section with short-answer questions.

  • The short-answer questions can:

    • ask you to briefly explain or discuss something OR
    • ask you to derive an answer analytically

  • The multiple choice section and short-answer section will be approximately equally weighted (i.e. each will be around 50 percent of the exam marks).

2 Review List

Lecture 1

  • Lecture 1 Slides: Probabilities, Expectations and Moments

  • Review Exercises

Lecture 2

  • Lecture 2 Slides: Inference

  • Review Exercises

Lecture 3

  • Lecture 3 Slides: OlS Assumptions

  • Lecture 3 Notes: OLS Theory

  • Review Exercises - Part I-II

  • If you are already comfortable with the linear/matrix algebra parts of the review exercises above, you do not need to go over the supplementary notes and exercises on matrix algebra that is included under Lecture 3 (e.g. Matrix Algebra Review - Part I-II and Matrix Algebra Exercises). Go over these supplementary notes only if you feel you are having difficulty with following the solutions of Review Exercises Part I-II.

Lecture 4

  • Lecture 4 Slides: Panel Data and Fixed Effects Estimation

Lecture 5

  • Lecture 5 Slides: Binary Choice Models

  • Lecture 5 Slides: Maximum Likelihood Estimation

  • Review of Probability Distribution Functions

  • Review Exercises - Binary Choice Models

Lecture 6

  • Lecture 6 Slides: Extremum Estimators

  • Lecture 6 Notes: ML Estimation for OLS Parameters

  • Review Exercises

Lecture 7

  • Lecture 7 Slides: Econometric Insights (Part I)

  • Lecture 7 Slides: Econometric Insights (Part II)

  • Lecture 7 Slides: Econometric Insights (Part III)

Lecture 8

  • Lecture 8 Slides: Econometric Estimation Pitfalls

  • Review Exercises 1

  • Review Exercises 2