ECO351 Introduction to Econometrics
(3 credits)
COURSE SYLLABUS
COURSE DESCRIPTIONFROM CATALOG
Reviews the theory of statistics and statistical techniques. Emphasizes the application of statistical models to economics. Covers regression analysis and estimation of economic models, including violations of the basic assumptions of the regression model, dummy variables, analysis of variance, cross section and time series data analysis, and index numbers.
COURSE OBJECTIVES
This is a first course in econometrics, which is the application of statistical methods to economic problems. This course is applied, in the sense that it deals with real data sets and real problems. Computer work is an integral part of the course. At the same time, parts of this course will be theoretical. An important way in which econometrics differs from statistics is that econometrics focuses on links between economic theory and statistical analysis. In particular, ECO351 will
emphasize the kinds of inferences that can and cannot be drawn from statistical analysis. Understanding when to use particular methods and what conclusions you can draw is as important as understanding how to do the analysis. The course provides the necessary tools to students to make estimation in a variety of business courses. Students who successfully complete this course will be prepared to take advanced econometrics at the undergraduate level.
PRE/CO-REQUISITE(S)
ECO 201, ECO 202, WRI 102 and any one of QBA 201, NGN 111, STA 201 or STA 202
LEARNING OUTCOMES
Upon completion of the course, students will be able to:
Explain large-sample properties of random variables and solve problems related to the central limit theorem. 1.
Design hypothesis tests and confidence intervals and appraise their suitability for drawing conclusions in a variety of business and economics contexts.
2.
Apply econometric models and procedures to business and economic data using computer programs. 3.
Select the appropriate econometric model for addressing econometric questions and defend their choices. 4.
Assess the validity of empirical analysis in a variety of business and economic contexts. 5.
REQUIRED TEXTBOOK
James H. Stock and Mark M. Watson (2012). Introduction to Econometrics (3rd edition). Pearson. 1.
I verify that I have reviewed and approved this syllabus for use in ECO351 course.
A Faculty Name Michael Malcolm
B Term/ Year Fall 2012
C Sections
CRN Course Days Time Location
11444 ECO351-01 UTR 3:00-3:50 SBM 005
D Instructor
Information Instructor Office Telephone Email
Michael Malcolm SBM 2120 515-2592 [email protected]
Office Hours:
• UTR: 1:00-3:00 and by appointment
E Other
Instructional Material and Resources
Textbook: James H. Stock and Mark M. Watson, Introduction to Econometrics, 3rd edition, Pearson, 2012.
My lecture notes will comprise the main material for the course. An electronic copy is posted to iLearn. Homework problems, old exams and data sets from class examples are also posted to iLearn. Email and iLearn are the main ways in which I will communicate with you, including to transmit course materials. You are responsible for checking your email at least daily.
You will need to use EViews for some of the homework exercises. You can either buy a copy at a substantially reduced price (please see the department secretary about this) or you can use any of the labs in the business building.
F Teaching and Learning Methodologies
The primary method of content delivery is lecture. There will also be a significant amount of hands-on practice in the classroom where students will engage in guided practice estimating econometric models on the computer. There is regular homework, consisting of both theoretical and empirical exercises, for students to practice and receive quick feedback on their
G Grading Scale, Grading Distribution, and Due Dates
Grading Scale
95– 100 4.0 A 77 – 79 2.3 C+
90 – 94 3.7 A- 73 – 76 2.0 C
87 – 89 3.3 B+ 70 – 72 1.7 C-
83 – 86 3.0 B 60 – 69 1.0 D
80 – 82 2.7 B- Less Than 60 0 F
Grading Distribution
Assessment Weight Date
Homework 12.5% Every class
Attendance 10% Every class
Mini-projects 12.5% December 4 & January 3
Midterm 1 10% October 4
Midterm 2 17.5% November 25
Midterm 3 17.5% December 23
Final Exam 20% January 15
Total 100%
H Explanation of Assessments
• Homework will be assigned after each class and is due at the beginning of the following class. Homework problems will include both theoretical exercises and empirical exercises using the computer. I will drop your three lowest homeworks, but late homework will not be accepted under any circumstances and missing homework will not be excused under any circumstances.
• Attendance is taken at the beginning of each class. You can miss class up to four times without any effect on your grade. Other than this, there are no excused absences for any reason whatsoever. Coming to class late twice counts the same as one absence.
• The first mini-project will involve analysis of a data set in response to a question that I present. For the second mini-project, you will formulate your own question, find your own data and do some brief analysis.
• The midterms will include both theoretical exercises and empirical exercises using the computer.
• The final exam is cumulative and will be held on the date determined by the registrar.
I Student
Academic Integrity Code Statement
Students are advised that violations of the Student Academic Integrity Code will be treated seriously and can lead to suspension or dismissal from the university. A notation of the academic integrity code violation can become part of the student’s permanent record.
Academic violations include but are not limited to:
• Plagiarism
• Inappropriate Collaboration
• Inappropriate Proxy
• Dishonesty in Examinations and Submitted Work
• Work Completed for One Course and Submitted to Another
• Deliberate Falsification of Data
• Copyright Violations
• Complicity in Academic Dishonesty
Students MUST read the Student Academic Integrity Code outlined in the AUS Catalog and agree to abide by the standards for academic conduct, students’ rights and responsibilities and procedures for handling allegations of academic dishonesty.
J Student
Responsibilities/ Behavioral Expectations
• Late homework is never accepted for any reason, nor will missing homework ever be excused.
• There are no excused absences for any reason whatsoever.
• You must stay for the entire class in order to receive attendance credit. Students who leave class early will not receive attendance credit regardless of the reason.
• There are no make-up exams. If you miss an exam with a valid excuse (documented illness or death in the family), the points will be shifted to other graded items. If you miss an exam without a valid excuse, you will receive a zero for the exam.
• You may not use your mobile phone or laptop computer during class. This includes text
messaging.
• You may be withdrawn from the class if you are absent for more than 15% of the class
meetings.
K Student – Instructor Interaction / Feedback
Students are encouraged to contact the instructor during office hours or via email. The instructor is willing and able to provide additional tutorials to a student who is struggling with the class material. In order to gather feedback for future course improvements, suggestions, comments and concerns regarding any aspect of this course will gladly be accepted, at any time during the
semester. In addition, a formal course evaluation will be administered at the end of the semester.
SCHEDULE
DATE Topic Lecture Notes Text
Sep 16 Introduction Unit 1 Chapter 1
Sep 18 Review of Probability Unit 2.1 Chapter 2
Sep 20 Review of Probability Unit 2.1 Chapter 2
Sep 23 Review of Probability Unit 2.1 Chapter 2
Sep 25 Review of Statistics Unit 2.2 Chapter 3
Sep 27 Review of Statistics Unit 2.2 Chapter 3
Sep 30 Hypothesis Testing and Confidence
Intervals Unit 2.3 Chapter 3
Oct 2 Hypothesis Testing and Confidence
Oct 4 Midterm 1 Units 1-2
Oct 7 Single-Variable OLS: Estimation Unit 3.1 Chapter 4
Oct 9 Single-Variable OLS: Estimation Unit 3.1 Chapter 4
Oct 11 Single-Variable OLS: Inference Unit 3.2 Chapter 5
Oct 14 Single-Variable OLS: Inference Unit 3.2 Chapter 5
Oct 16 Single-Variable OLS: Theory Unit 3.3 Chapter 5
Oct 18 Single-Variable OLS: Theory Unit 3.3 Chapter 5
Oct 21 Multiple Regression: Estimation Unit 4.1 Chapter 6
Oct 23 Multiple Regression: Inference Unit 4.2 Chapter 7
Oct 25 Multiple Regression: Inference Unit 4.2 Chapter 7
Nov 4 Multiple Regression: Multicollinearity Unit 4.3 Chapter 7
Nov 6 Multiple Regression: Omitted and
Irrelevant Variables Unit 4.3 Chapter 7
Nov 8 Multiple Regression: Model Selection Unit 4.4 Chapter 7
Nov 11 Specification: Dummy Variables Unit 5.1 Chapter 8
Nov 13 Specification: Polynomial Expansions Unit 5.2 Chapter 8
Nov 18 Specification: Logged Variables Unit 5.3 Chapter 8
Nov 20 Specification: Interactions Unit 5.4 Chapter 8
Nov 22 Specification: Interactions Unit 5.4 Chapter 8
Nov 25 Midterm 2 Units 3-5
Nov 27 Instrumental Variables Unit 6.1 Chapter 12
Nov 30 Instrumental Variables Unit 6.2 Chapter 12
Dec 4 Instrumental Variables Unit 6.2 Chapter 12
Dec 9 Limited Dependent Variables Unit 6.3 Chapter 11
Dec 11 Limited Dependent Variables Unit 6.3 Chapter 11
Dec 13 Limited Dependent Variables Unit 6.3 Chapter 11
Dec 16 Panel Regression Unit 6.4 Chapter 10
Dec 18 Panel Regression Unit 6.4 Chapter 10
Dec 20 Panel Regression Unit 6.4 Chapter 10
Dec 23 Midterm 3 Unit 6
Dec 27 Causality Unit 7.1 Chapter 9
Dec 30 Causality Unit 7.1 Chapter 9
Jan 3 Validity Unit 7.2 Chapter 9
Jan 6 Experimental Designs Unit 7.3 Chapter 13
Jan 7 Quasi-Experimental Designs Unit 7.4 Chapter 13
Jan 8 Quasi-Experimental Designs Unit 7.4 Chapter 13
Jan 9 Conclusions and Review Unit 8
