Principles of Econometrics I: Using EViews
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This web-based course is designed to introduce working professionals and students to the concepts of basic econometrics without requiring the use of advanced matrix algebra, calculus or statistics. The course is the first in a two-course series which provides a comprehensive introduction to econometric techniques that are valuable not only to business and economics students, but to students of other social and physical sciences as well.
Course Specifications:
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Length:
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12 weeks
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Weekly time commitment:
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Approximately 5 to 10 hours
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Prerequisites:
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Knowledge of basic algebra and basic
statistics is recommended.
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Tuition:
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US $1,595
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Additional expenses:
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Textbook including EViews software.
U.S. Edition is approximately $75.
Foreign Edition prices vary by location.
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Course Materials - Text and Software
The course is based upon the text, Basic Econometrics, by Damodar N. Gujarati.
Published by McGraw-Hill Higher Education, the text is in its 4th Edition. The
course utilizes EViews, a sophisticated and user-friendly statistical package,
which accompanies the text.
The text can be purchased directly from McGraw-Hill Higher Education at http://www.mhhe.com/.
The ISBN is 0072565705.
System Requirements
The minimum system requirements necessary to complete the course are as follows:
- A 486X-based computer
- Microsoft Windows 95 or later or Windows NT 4.0 or later.
- A minimum of 16MB of memory with 32MB recommended
- A VGA, super VGA or compatible display
- Access to the Internet and an email account with the ability to send and receive
large Microsoft Excel and Word files
Course Format
The course runs 12 weeks. Each week the student will read a chapter in the text
and the associated lecture notes and complete assigned exercises using the EViews
software. A weekly quiz will enable the student to test his or her knowledge of
the chapter material. Students will also complete an independent project in order
to apply the econometric techniques they have learned to a real world situation
of personal or professional interest.
Course Content
Unit 1: Introduction to Econometrics and Single-Equation Regression Models
The first unit introduces the student to the methodology of econometrics. Topics
covered include regression analysis, the foundation of econometric theory and
practice, sources and limitations of data, and statistical concepts. The terminology
and notation used in the text is defined. Includes demonstrations of the EView
software's basic features.
Unit 2: Two-Variable Regression Analysis: Some Basic Ideas
Unit two discusses the basic theory underlying bivariate, or two-variable regression.
Key concepts are defined including the population regression function, linearity,
the stochastic error term and the sample regression function. A review of statistical
concepts continues and elementary tools of regression analysis are demonstrated
using the EViews software.
Unit 3: The Classical Linear Regression Model
This unit introduces the classical linear regression model and its underlying
assumptions. The ordinary least squares method of estimation is discussed in detail
and the properties of least squared estimators are outlined. A measure of goodness
of fit - the coefficient of determination - is defined. The statistics review
continues as do examples and problems using the EViews software.
Unit 4: Interval Estimation and Hypothesis Testing
Unit four covers interval estimation and hypothesis testing in detail. Other topics
include the t-test and chi-squared test of significance, selecting the appropriate
level of significance, and reporting and evaluating the results of regression
analysis. Students complete the review of statistical concepts. Additional techniques
of data analysis are demonstrated in EViews.
Unit 5: Extensions of the Two-Variable Linear Regression Model
This unit introduces several of the finer points of the classical linear regression
model. Regression through the origin is explained, as is the importance of scale
and the units of measurement. Several functional forms of regression models are
introduced. Students will also learn more new skills in EViews.
Unit 6: Multiple Regression Analysis
Unit 6 introduces the three-variable model of regression, its notation and assumptions.
Other topics include interpreting the multiple regression equation, partial regression
coefficients, model specification bias, polynomial regression models, and adjusted
R2. An example using the Cobb-Douglas production function is discussed in detail.
More problems and examples are provided in EViews.
Unit 7: Multiple Regression Analysis: The Problem of Inference
The problem of inference as it relates to multiple regression is discussed in
this unit. Other issues include normality assumptions, hypothesis testing, and
testing for significance in multiple regression. Testing for structural stability
and testing the functional form of the regression are explained, as is prediction
with multiple regression. Skill development in EViews continues.
Unit 8: Dummy Variables
Unit 8 introduces the use of dummy variables as a qualitative indicator, and the
technical issues associated with their use. The use of dummy variables in seasonal
analysis is also detailed. Other topics include ANOVA and ANCOVA models, piecewise
linear regression and panel data regression models. More practical techniques
are demonstrated using EViews.
Unit 9: Multicollinearity
This unit details the problem of multicollinearity, which arises when independent
variables are correlated. A distinction between perfect and high, but imperfect
multicollinearity is also illustrated, as are the theoretical and practical consequences
of multicollinearity. Methods of detection and measures of remediation are addressed.
Techniques for identifying and resolving the problem of multicollinearity are
demonstrated using EViews.
Unit 10: Heteroscedasticity
Unit 10 addresses the problem of heteroscedasticity, which arises when the variance
of the disturbance terms is not constant. Students will investigate its causes,
consequences and cures, and discover the method of weighted least squares. Examples
and problems in EViews illustrate identification and remediation techniques.
Unit 11: Autocorrelation
The problem of autocorrelation, which is the correlation between successive observations
over time, is addressed in this unit. The nature of autocorrelation is investigated,
as are tests for identifying and methods for dealing with the problem. The class
will discuss the distinction between model misspecification and pure autocorrelation,
as well as the method of generalized least squares. EViews examples are provided.
Unit 12: Econometric Modeling
In this final unit, model specification and diagnostic testing issues are addressed.
Students will discuss model selection criteria, errors of measurement and data
issues, as well as the characteristics of a successful model. Other topics include
specification errors and the consequences of including an irrelevant variable
or omitting a relevant variable. Diagnostic testing techniques are illustrated
in EViews.
Independent Econometric Project
At the beginning of the term, students select a topic of personal or professional
interest. On a weekly basis, they will use EViews and the new econometric skills
introduced to analyze their data. At the end of the term, students submit a final
summary report which demonstrates their understanding of the concepts covered
in the course and their ability to apply these newly acquired techniques of analysis
using the EViews software.
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