Introduction to Econometrics
ECON 141-01 – Spring 2014
Section 1 meets TTh 10:30 – 11:45 am in Amador Hall 240
Professor Information:
Professor: Antonio Bojanic
Office: Tahoe 3013
Office Hours: Tue, Thur: 4:30 – 5:30 pm, or by
appointment
Email: antonio.bojanic@csus.edu
Phone: (916)278-5588
Prerequisites:
Introductory to Macroeconomics and Microeconomics (i.e., ECON 1A and 1B or their equivalents),
Quantitative Economic Analysis (i.e., ECON 140 or its equivalent), and either Intermediate Macroeconomics or
Microeconomics (i.e., ECON 100A or 100B, equivalents).
This is a data-intensive class, so you’ll need an econometric software package. I use Eviews and the university
does hold site licenses for this package, so I encourage you to use it as well. You are free, however, to utilize
any software package you like, provided it does the various calculations needed. Excel is not an appropriate tool
to use as it is difficult to use and is not able to perform some calculations that will be covered in class.
Required Materials:
Textbooks : Basic Econometrics, by Damodar N. Gujarati and Dawn C. Porter, McGraw-Hill, 2009.
Evolution of the Bolivian Economy: A Time Series Approach, by Antonio Bojanic, Kendall Hunt
Publishing Co., 2013
Course Overview:
General Goal: The purpose of this course is to introduce you to the fundamentals of econometrics (i.e.,
regression analysis applied to economic issues). Regression analysis is a useful statistical tool used by many
decision-makers to assess the relationship between various factors.
Course Objectives:
The objectives of the course are:



to develop an understanding of the process followed when building an econometric model
to apply econometric tools to data utilizing statistical software
to develop an understanding of diagnostic tools
Tips for Success
Very important to have good class notes – required textbooks should be a complement to class notes, not a
substitute for them. Exams come from my notes so it is essential to have clear and concise class notes
Read the textbook material before class time
Don’t miss classes
Don’t be afraid to ask questions
Grading and Exam Procedures:
Midterm 1
Midterm 2
Paper
Final exam
Homework
20%
20%
25%
25%
10%
Paper: The written assignment for this class may be on any topic that interests you. To count, the paper must
include a regression equation utilizing the tools we will learn during class. The format of the paper must be
the standard format found in any economics journal paper. I encourage you to refer to the guidelines for
accepted manuscripts for the American Economic Review and follow the suggestions of this economic
journal (http://www.aeaweb.org/aer/styleguide.pdf). I will grade the paper on the extent to which you utilize
concepts learned in class, the quality of the writing, and the effort and dedication you give to the
assignment. You should be prepared to stand up in front of the class and answer a few questions from your
classmates and from myself.
Exams: There will be three exams for this class, two midterms and the final. The two midterms cover the most
recent material from the text and from class lectures. The final exam will be given during finals week and is
cumulative. The exams will consist of problems you will analyze using the tools learned in the course. All
exam questions come from class lectures/discussions.
Calculator: Make sure to bring a non-programmable calculator on exam days.
Homework: Several homework exercises will be assigned throughout the semester. Most will utilize data from
Bojanic’s textbook and others will utilize data that will be provided in class. Homework is to be handed in at a
date established in class. Homework handed in late will not be accepted.
Exam and Paper Dates:





Midterm 1:
Midterm 2:
Paper:
Final exam:
Thursday, March 6
Thursday, April 17
Thursday, April 24
Thursday, May 22 at 10:15 – 12:15 pm
Grade Scale:
I round up to the half point, then use the following scale:
A = 100 – 92.5
A- = 92 – 90
B+ = 89.5 – 88.5
B = 88 – 82.5
B- = 82 – 80
C = 78 – 72.5
C- = 72 – 70
D+ = 69.5 – 68
D = 67.5 – 60
F = 59.5 – 0
C+ = 79.5 – 78.5
Responsibilities and Classroom Etiquette:
Professor responsibility: I am responsible for facilitating the learning process for students who are committed
to learning. I am responsible for determining the extent to which students have met the stated objectives in this
course. I am expected to be fair, courteous and respectful, to be responsive to student needs, and not to
discriminate based on race, gender, ethnicity, or sexual or religious preference. Grades are assigned based on
my professional judgment of the quality of your work and are not subject to negotiation.
Student responsibility: You are responsible for your learning outcomes and your performance on all
assignments. You are responsible for contacting the professor if you are struggling with the material. You are
expected to attend all class periods, be active learners who contribute to the classroom discussion/activities, and
be courteous and respectful of the other students and the professor. This includes arriving on time, turning off
cell phones/pagers/alarms, refraining from talking (or whispering) to your neighbor if the professor or another
student is addressing the class, refraining from packing up your bags prior to the end of class, refraining from
coming and going in the middle of class, etc. Students are responsible for knowing the policy regarding
attendance and disruptive behavior. Cheating is taken very seriously and will not be tolerated. Please refer to
the University’s Academic Dishonesty Policy in your Catalog to inform yourself of the consequences of such
action.
General Outline of Course
The following is a brief list of topics to be covered and corresponding textbook chapters and/or supplemental
readings. The Gujarati/Porter book should be utilized for theory and the Bojanic book for exercises.
I.
Overview of regression analysis (Chapter 1 Gujarati)
a. What is regression analysis
b. Types of variables, types of data
c. The estimated equation
d. Exercises
II.
Some statistical principles (Appendix A Gujarati)
a. Probability distributions
b. Sampling, estimation
c. Exercises
III.
Ordinary least squares (OLS) (Chapter 3 Gujarati)
a. Single and multivariate OLS regression models
b. Quality and fit of specification model
c. Exercises
IV.
Utilizing regression analysis (Chapters 2, 3 Gujarati)
a. Basis steps
b. Exercises
V.
Classical model (Chapters 3, 4 Gujarati)
a. Basic assumptions
b. Gauss-Markov Theorem
c. Exercises
VI.
Hypothesis testing (Chapter 5 Gujarati)
a. The t-test
b. Exercises
VII.
Specifying the model (Chapters 6, 7 Gujarati)
a. Omitted variables
b. Irrelevant varibles
c. Exercises
VIII.
The functional form of the regression model (Chapter 7 Gujarati)
a. Meaning of constant term
b. Alternative functional forms
c. Lagged, dummy variables
IX.
Multicollinearity (Chapter 10 Gujarati)
a. Meaning of multicollinearity, degrees of multicollinearity
b. Consequences, detection, and remedies for multicollinearity
c. Exercises
X.
Serial correlation (Chapter 12 Gujarati)
a. Meaning of serial correlation, degrees of serial correlation
b. Consequences, detection, and remedies for serial correlation
c. Exercises
XI.
Heteroskedasticity (Chapter 11 Gujarati)
a. Meaning of heteroskedasticity, degrees of heteroskedasticity
b. Consequences, detection, and remedies for heteroskedasticity
c. Exercises
I reserve the right to alter the above schedule if necessary.