Preliminary Course Outline Introduction to Econometrics 321 Department of Economics University of Waterloo Spring 2013

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1 Preliminary Course Outline Introduction to Econometrics 321 Department of Economics University of Waterloo Spring 2013 Instructor: Mikko Packalen O ce Hours: Tuesdays, 11.30am-1pm, HH205, and by appointment Class Hours: Tuesdays and Thursdays, am, MC packalen@uwaterloo.ca [put "Econ 321" in the subject line; from your.uwaterloo account]. O ce Phone: ext Course Description: Introduction to formal statistical analysis of choice-based behavior. Main Learning Objective: To gain the ability to identify whether an estimated e ect is a good estimate of the unknown true e ect. Reaching this objective gives student the skill to determine whether analyses in news and research articles are informative by rst mapping the analysis to the regression framework and then forming beliefs about the relationship between observed and unobserved explanatory variables. Prerequisites: Good command of Probability Theory, Statistics and Calculus. Familiarity with Regression Analysis. Course materials: Problem Sets, Lectures, Class Notes, Slides, and Textbooks. These are all strongly complementary to one another; none of them is a substitute to another. Problem Sets: Distributed weekly through learn.uwaterloo.ca. Class Notes and Slides: Distributed through learn.uwaterloo.ca. The distributed slides do not include all slides shown in class. The notes are not a substitute to attending lectures. Recommended Textbooks: - Stock, J. H. and M. W. Watson, Introduction to Econometrics, any edition. - The textbook on probability theory student used in the prerequisite class on probability. - The textbook on statistics student used in the prerequisite class on statistics. - The textbook on calculus student used in the prerequisite class on calculus. Expectations: The class meetings are only an introduction to each topic covered in this course. For this reason class attendance should represent less than 30% of the student s weekly e ort in studying for this class. The course progresses fast and the course material is cumulative both in terms of what is studied during the semester and what is studied in the prerequisite classes; success in the course thus generally requires a weekly e ort from the student outside of class and a good understanding of the probability 1

2 theory, statistics, and calculus covered in the prerequisite classes. Without the weekly e ort outside of class a student should not expect to either learn the material or receive a passing grade for the class. Similarly, students without a good understanding of probability theory, statistics, and calculus covered in the prerequisite courses should not expect to either learn the material in this course or receive a passing grade for the class. Class Topics (The numbers in parentheses refer to sections in the 3rd edition of the textbook. Previous editions have similar sections but may be labeled/numbered slightly di erently. A careful studying of these sections is recommended): Week 1: The Linear Regression Model Introduction (1-3). The Linear Regression Model (4.1) Problem Set 1 Due Week 2: The OLS Estimator The OLS Estimator (4.2, Appendix 4.2) Consistency of the Least Squares Estimator (4.4, Appendix 4.3) Problem Set 2 Due Week 3 Computer exercise 1 Midterm 1 Problem Set 3 Due Week 4: Omitted Variable Bias Omitted Variable Bias (6.1, Appendix 6.1) Problem Set 4 Due Week 5: Hypothesis Testing The Sampling Distribution of the LS Estimator (4.5, Appendix 4.3) Hypothesis Testing ( ) Problem Set 5 Due 2

3 Week 6 Computer exercise 2 Midterm 2 Problem Set 6 Due Week 7: Solution 1 to Omitted Variable Bias Linear Regression with Multiple Regressors ( , ; 4.3, 6.4, 7.5; 5.4; 8) Tests of Joint Hypotheses in Multiple Regression ( , 7.4) Problem Set 7 Due Week 8: Solution 2 to Omitted Variable Bias Panel Data Analysis ( ) Problem Set 8 Due Week 9 Computer Exercise 3 Midterm 3 Problem Set 9 Due Week 10: Further Threats to Analysis Measurement Error (9.1) Simultaneous Causality (9.2) Problem Set 10 Due Week 11: Solution 3 to Omitted Variable Bias The IV Estimator (12.1) The Sampling Distribution of the IV Estimator (Appendix Appendix 12.4) Tests for Instrument Validity (12.3) Examples of IV Analyses ( ) Problem Set 11 Due 3

4 Week 12: Solution 4 to Omitted Variable Bias Estimation of Treatment E ects in Experiments ( ) Quasi-Experiments ( ) Problem Set 12 Due Exams: Midterm Exam 1: Thursday, May 23rd, in Class. Midterm Exam 2: Thursday, June 13th, in Class. Midterm Exam 3: Thursday, July 4th, in Class. Final Exam: TBA Each exam covers topics discussed in all previous classes and in the associated homeworks as well as in the associated sections of the textbook (see above) and the prerequisite classes. Missed Exams: Only a properly and timely documented illness or death of an immediate family member is a valid reason for missing an exam. The weight of a missed Midterm Exam is shifted on the Final Exam if the students submits proper documentation in a timely manner. Student who misses an exam and does not submit proper documentation in a timely manner receives the grade 0 for the exam. A student who misses the Final Exam for a valid and properly documented reason can attend the Deferred Final Exam Session arranged by the economics department simultaneously for all economics classes for the Spring 2013 term. There are no other Make-Up Final Exams and there are no Make-Up Midterm Exams. Final Grade: The nal grade is calculated as a weighted average of exam performance with the following weights: 20% Midterm Exam 1 20% Midterm Exam 2 20% Midterm Exam 3 40% Final Exam Institutional Required Statements: Academic Integrity: in order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. 4

5 Discipline: A student is expected to know what constitutes academic integrity, to avoid committing academic o enses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an o ense, or who needs help in learning how to avoid o enses (e.g., plagiarism, cheating) or about rules for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. When misconduct has been found to have occurred, including writing exams in a section that you are not registered in, disciplinary penalties will be imposed under Policy 71 Student Discipline. For information on categories of o enses and types of penalties, students should refer to Policy 71 - Student Discipline, Grievance: A student who believes that a decision a ecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4, In addition, consult vances-faculty-arts-processes for the Faculty of Arts grievance processes. Appeals: A student may appeal the nding and/or penalty in a decision made under Policy 70 - Student Petitions and Grievances (other than regarding a petition) or Policy 71 - Student Discipline if a ground for an appeal can be established. Read Policy 72 - Student Appeals, Academic Integrity Website(Arts): Academic Integrity O ce uwaterloo): Note for students with disabilities: The AccessAbility Services (AS) O ce, located in Needles Hall Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AS O ce at the beginning of each academic term. 5