ECO 310 Econometrics
Spring 2013
Dr. Robert Jantzen
Economics Department


Where and When
Course Description
Course Objectives
Teaching Method
 Texts and Stat Tables
Course Grading
Term Project
Course Outline
Data & Software
Contact Information
Internet Data
College Policy for All Courses

Where and When

In the Spring of 2013 this course meets at 9:30 a.m. on Mondays and Wednesdays in Amend 107. Classes begin on 1/16/13.

Course Description

A laboratory approach to multivariate research, with an emphasis on economic applications.  A review of the basic concepts of regression analysis and the testing of hypotheses, and the statistical problems that arise when the simple regression model's assumptions are violated by the data or the model being analyzed, and appropriate countermeasures.  An examination not only of the classical regression model, but also qualitative choice and simultaneous equations models.  Instruction in the basics of computerized data analysis, including collection, coding and statistical programming.  PREREQUISITE:  any one-semester Introduction to Statistics course.  3 credits.

Course Objectives

The primary objective of this course is to impart to students a working knowledge of how best to analyze simple and multivariate relationships using a variety of regression models.  Students will learn how to test and correct for a wide variety of standard statistical problems that appear when data is analyzed.  These include serial correlation, multicollinearity, heteroskedasticity, specification bias, measurement error, among others.  Students will not only master the statistical theory, but also the computer  programming skills necessary to field-test multivariate models.

Teaching Method:

This course will rely principally on lecture and discusssion, augmented by statistical programming demonstrations and labs.


Studenmund, A. H., Using Econometrics: A Practical Guide (6th. ed.)  NY:  Pearson Addison Wesley, 2011.   The textbook is
available at the Iona College bookstore (suggested retail $ 200), as an E-textbook from the publisher @ for $85, and as an international paperback edition from Langton Info
Services @ for $120.  Prices are approximate.

               Jantzen, R.  A Brief Guide to the Gretl Program.  Unpublished manuscript, 2012.

               Jantzen, R.  A Brief Introduction to Multiple Regression.   Unpublished manuscript, 2004.

               Jantzen, R.  A Brief Guide to Classical Regression "Problems".  Unpublished manuscript, 2004.

               Additional required readings will be assigned by the instructor at appropriate times in the course.

Course Requirements and Grading:

  Student grades in this course will reflect assessment in the following areas:

 Homeworks        (relative weight = .2)
 Exam # 1            (relative weight = .3)
 Exam # 2            (relative weight = .3)
 Final Exam         (relative weight = .3)
 Term Project      (relative weight = .3)

    All students must complete the term project, including a short oral presentation during the last week of classes.  The lowest grade on one of the above 5 areas, except for the term project, will be
dropped, however, when the final course grade is determined. Homework assignments will receive credit only if completed on time as scheduled, but may be submitted via email.   All exams will be open book/open notes and will take place in an Iona computer lab.  Make-up exams will be available only to those students who have notified the instructor prior to the scheduled exam date (an email to the instructor leaving a message is adequate).

    Academic dishonesty will be penalized heavily.  Plagiarism (the copying of text from other sources without the use of quotation marks) and/or cheating will result in a grade of F for the paper/exam involved.   In addition, students having excessive absences (6 or more) will receive the grade of FA (failed for absence).   Being late to a class will count as an absence.

Term Project:

I.  Description

    The fundamental purpose of the term paper is for the student to utilize multiple regression analysis to assess the relationships between a dependent variable and at least three explanatory variables.  The term project must be written in the student's own words, be typed (double spaced) and contain an appendix that includes all of the  statistical outputs utilized to generate the tables and tests described in the paper.   Term projects must be submitted to the instructor via email as a single MS-Word or Adobe PDF file.  In addition, each student must make an oral presentation of the term project to the class during the last week of classes.

II.  Organization

The term paper for this course must contain:

    A.  an Introduction that briefly explains the purpose of the paper.

    B.  a Review of the Literature section that reviews the methods and findings of at least one other study that has already examined the topic of your study.

    C. a Data and Methodology section that explains the sources of the data, their time and scope, and the model to be estimated.  This section must also detail the expected relationships between the variables, and expound on any anticipated statistical problems and their appropriate corrections.

    D.  an Empirical Results section that provides and discusses:

              i.   descriptive statistics concerning the model's variables.

              ii.  an analysis of tests for heteroskedasticity or serial correlation, and corrections, if appropriate.

              iii.  F or Likelihood ratio tests on the overall model

              iv.  overall goodness of fit statistics.

              iv. T tests for each population regression coefficient.

              v.  estimated coefficients and their confidence intervals, using the appropriate regression results.

              v.  standardized coefficients.

              vi.  an analysis of the likely effects of specification bias on the coefficients of the estimated model.  Specifically, identify one plausible explainer that was not included in the estimated model and how its exclusion would affect the coefficients that were estimated.

    E. a Summary and Conclusions section that highlights the key findings and policy implications of the study, if any.

Click on the following link for an example of a sample project: sampleproject.doc

III.  Data:

   To satisfy the term project requirement, students must collect and analyze their own data.  Click here for information on how to search, download and organize data from the web.


Typical Course Sequence:
Homework Assignment
1/16 & 1/21 Introduction Chapter 1. Homework 1 and  Homework 2
1/21 and 1/28
Multiple Regression Chapters 2 and 3. Homework 3 and  Homework 4
2/4 and 2/11
Hypothesis Testing Chapter 5. Homework 6Homework 7 and Homework 8
2/25 and 3/4
Dummy Variables and Nonlinear Models Chapter 7. Homework 9 and Homework 10
Regression Assumptions and Estimator Properties Chapter 4. Homework 5
Specification Bias Chapter 6. Homework 11
Multicollinearity Chapter 8. Homework 12
Heteroskedasticity & Normal Residuals Chapter 10. Homework 13
Serial Correlation Chapter 9. Homework 14
Qualitative Choice Models Chapter 13. Homework 15
Panel Data Models Chapter 16. Homework 16
Forecasting Trend Models Chapter 15 and Handout Homework 17

Software and Data:

       The Gretl econometric program will be the "platform" used by this course to analyze data.  The program is a freeware open-sourced program that performs a wide variety of statistical tests that economists utilize.  Information on how to download, install and operate the program can be found by clicking here.

       Assigned homeworks will contain links to the Excel data sets needed for completion. Bear in mind that the Gretl program can only process Excel data sets that have been saved as MS Excel Comma Delimited File (csv) Worksheets.

Contact Information:
Instructor:  Robert Jantzen, Ph.D.
Professor, Department of Economics
Office Location: Economics Department, Spellman Hall, 2nd floor
Office Hours: M, W 1:30 - 2:30 p.m., by appointment.
Phone:  (914)637-2731.
Fax:  (914)633-2511.

College Policy for all courses and students: (full explanations of policy may be found in the College Catalog)

Plagiarism:  Is the unauthorized use or close imitation of the language and thoughts of another author/person and the representation of them as one's own original work.  Iona College policy stipulates that students may be failed for the assignment or course, with no option for resubmission or re-grading of said assignment.  A second instance of plagiarism may result in dismissal from the College.

Attendance:  All students are required to attend all classes.  Iona has an attendance policy for which all students are accountable.  While class absence may be explained it is never excused.  Professors may weigh class absence in the class grade as they see fit.  Failure to attend class may result in a failure of the class for attendance(FA), when the student has missed 20% or more of the total class meetings. The FA grade weighs as an F would in the final official transcript.

Course and Teacher Evaluation(CTE):  Iona College now uses an on-line CTE system.  This system is administered by an outside company and all of the data is collected confidentially.  No student name or information will be linked to any feedback received by the instructor.  The information collected will be compiled in aggregate form by the agency and distributed back to the Iona administration and faculty, with select information made available to students who complete the CTE.  Your feedback in this process is an essential part of improving our course offerings and instructional effectiveness.  We want and value your point of view.*
NOTE* You will receive several emails at your Iona email account about how and when the CTE will be administered with instructions how to proceed.

Economics Department || Iona College