Econometrics Course Home Page

ECO 310 Econometrics
Spring 2006 Dr. Robert Jantzen
Economics Department

Where and When	Course Description	Course Objectives
Teaching Method	Texts and Stat Tables	Course Grading
Term Project	Course Outline	Homeworks
Data & Software	Contact Information	Internet Data
	Announcements

Where and When

In the Spring of 2006, this course meets at 9 a.m. on Mondays and Wednesdays in Doorley 117, and on Thursdays in McSpedon 23.

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.

Texts:

Pindyck, R. S. and Rubinfeld, D. L. Econometric Models and Economic Forecasts: 4th Ed. NY: McGraw-Hill, 2000. This text is available in both hardcover and paperback (in the International edition).

Jantzen, R. A Brief Guide to the EALimdep Program. Unpublished manuscript, 2002.

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 Unpublished manuscript, 2004.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 Unpublished manuscript, 2004.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:

Homework will be assigned nearly every class and students are strongly urged to complete those assignments. However, student grades in this course will reflect assessment only in the following areas:

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

The final course grade will be computed by taking the weighted average of the best 3 of the above 4 grades. Make-up exams will be available only to those students who have notified the instructor prior to the scheduled exam date ( a phone call to 637-2731 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 (10 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. The following describes how the term project should be organized, and what information must be included in each section:

Section Content

I. Introduction Provide a brief overview of the term paper’s research objectives and findings.

II. Literature Review Reviews the methods and findings of at least one other study that has already examined the topic of your study.

III. Data and Method 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.

IV. Empirical Results Provides:

descriptive statistics concerning the model's variables, e.g., means, standard deviations, ranges, etc.

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

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

overall goodness of fit statistics.

standardized coefficients and elasticities.

an analysis of the likely effects of specification bias in the model estimated.

V. Summary Highlights the key findings and policy implications of the study, if any.

II. Databases:

To satisfy the term project requirement, you may collect and analyze your own data or you may use one of the Excel
databases listed below. Click here for information on how to search, download and process data from the web.

The following Excel databases can also be used to complete the term project. Note that the databases are not in
Excel 4.0 Worksheet format because they contain multiple worksheets (i.e., both a file information and a data worksheet).
In you want to use EALimdep to analyze the data contained in any of the following databases, you must first save only the
data worksheet to a separate file in Excel 4.0 Worksheet format (click here for more info about preprocessing).

USDATA.XLS database contains monthly (starting in January 1992) US data for a variety of financial, production,
government and consumer behavior series. Sources are various Federal agencies.

GDPHISTTABLE.XLS database contains quarterly US values for GDP derived production values. Source is http://www.economic-indicators.com/.

CITYSTATE.XLS database contains a variety of economic, social and demographic variables for the largest US cities
as well as for the states (plus Washington, D.C). Source is University of Virginia's Fisher Library @
http://fisher.lib.virginia.edu/ccdb/state94.html.

WORLD95.XLS database containing 1995 population, health, income, education, religion, etc. information for 109 countries. Source is SPSS.

COUNTRY.XLS database containing 1992 population, health, income, etc. information for 122 countries. Source is SPSS.

NETWORK.XLS database containing size, occupancy, ownership, managed care penetration, etc. data for national sample of 235 hospitals (assembled by Loubeau and Jantzen).

MBADATA.XLS database containing enrollment, student demographics, faculty characteristics, tuition, accreditation status, etc. for the population of 624 graduate business (MBA) programs in the US. Data were compiled for 1988 and 1995 (by Jantzen).
STATBOOK.XLS database containing hospital financial and service characteristics, as well as market characteristics, for a random sample of 717 private US hospitals (assembled by Jantzen and Loubeau).

Typical Course Sequence:

Week (approximate)	Topic:	Reading:	Homework Assignment
1.	Introduction	Chapter 1.	Homework 1
1.	Background	Chapter 2.
2-3.	Two-Variable Regression	Chapter 3.	Homework 2, Homework 3 and Homework 4
4-5.	Multiple Regression	Chapter 4.	Homework 5, Homework 6 and Homework 7
6.	Dummy Variables and Nonlinear Models	Chapter 5.	Homework 8, Homework 9 and Homework 10
7-9.	Autocorrelation and Heteroskedasticity	Chapter 6.	Homework 11, Homework 12, Homework 13 and Homework 14
10.	Specification Bias	Chapter 7.	Homework 15
11-12.	Qualitative Choice Models	Chapter 11.	Homework 16
13-14.	Simultaneous Equations Models	Chapter 12.	Homework 17
14-15.	Forecasting Models	Chapter 8.	Homework 18

Software and Data:

The EALimdep econometric program will be the "platform" used by this course to analyze data. The program is a "freeware" student version of a sophisticated regression package widely used by economists. 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 EALimdep program can only process Excel data sets that have been saved as MS Excel 4.0 Worksheets.

Contact Information:

Instructor:                Robert Jantzen, Ph.D.
                               Professor, Department of Economics
Office Location:       Economics House, 34 Hubert Place
Office Hours:           Monday, Wednesday and Thursday, 11 a.m. - Noon, by appointment.
Phone:                     (914)637-2731.
Fax:                         (914)633-2511.
E-Mail:                    RJantzen@Iona.edu
Web-Page:              www.iona.edu/faculty/rjantzen/homepage.htm

Economics Department || Iona College

I. Introduction	Provide a brief overview of the term paper’s research objectives and findings.
II. Literature Review	Reviews the methods and findings of at least one other study that has already examined the topic of your study.
III. Data and Method	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.
IV. Empirical Results	Provides: descriptive statistics concerning the model's variables, e.g., means, standard deviations, ranges, etc. an analysis of tests for heteroskedasticity or serial correlation, and corrections, if appropriate. estimated coefficients and their confidence intervals, using the appropriate regression results. overall goodness of fit statistics. standardized coefficients and elasticities. an analysis of the likely effects of specification bias in the model estimated.
V. Summary	Highlights the key findings and policy implications of the study, if any.