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JOHN CABOT UNIVERSITY

COURSE CODE: "EC 360"
COURSE NAME: "Econometrics"
SEMESTER & YEAR: Spring Semester 2012
SYLLABUS

INSTRUCTOR: Ciferri Davide
EMAIL: [email protected]
HOURS: MW 19:00-20:15
TOTAL NO. OF CONTACT HOURS: 45
CREDITS:
PREREQUISITES: Prerequisites: EC 201, EC 202, MA 209
OFFICE HOURS:

COURSE DESCRIPTION:

This is an introductory course in econometrics and it will introduce students to the basic principles of the filed. The course attempts to provide a balance between theory and applied research with a focus on learning the usage of econometrics in practise. Students will present a project that demonstrates their skills to use computer software as well as to describe the empirical results obtained.

SUMMARY OF COURSE CONTENT:

Single-equation regression model, classical normal linear regression model, statistical inference, hypothesis testing, misspecification, autocorrelation, heteroscedasticity, multicollinearity, dummy variable regression models, qualitative response regression models.

LEARNING OUTCOMES:


Students should gain a solid foundation in both theoretical and empirical econometrics.  and the basic skills needed to develop an empirical research.

TEXTBOOK:
Book TitleAuthorPublisherISBN numberLibrary Call NumberCommentsFormatLocal BookstoreOnline Purchase
Basic EconometricsD.N. Gujarati and D.C. PorterMcGraw-Hill978-007-127625-2     
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
Exam I<span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Review of some Statistics and mathematical tools, </span><span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Single and Two variable Regression Model: </span>15%
Exam II<span style="font-family: tahoma, sans-serif; font-size: 8.5pt;"><span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Multiple Regression&nbsp; Analysis</span> and Hypothesis Testing</span>15%
Exam III<span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Dummy variables, </span><span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Multicollinearity, </span><span style="font-family: tahoma, sans-serif; font-size: 8.5pt;">Heteroscedasticity</span>15%
ProjectModel estimation and analysis of empirical results using econometric&nbsp;software<br /> &nbsp;15%
Final 40%

-ASSESSMENT CRITERIA:

A: 90 to 99%

B: 80 to 89%

C: 70 to 79%

D: 60 to 69%

F: below 60%


A:  Work of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensiveknowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.

B:  This is highly competent level of performance and directly addresses the question or problem raised.

There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.

C:  This is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.

D:  This level of performances demonstrates that the student lacks a coherent grasp of the material.

Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.

F: This work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.


-ATTENDANCE REQUIREMENTS:

Strongly encouraged

ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.

SCHEDULE

  Week 1:  Jan 16, 18

 

 

Review of some Statistics and mathematical tools: Appendix A, B

 

Single-Equation Regression Model: Chapters 1 and 2

 

Week 2:  Jan 23, 25

 

Two-Variable Regression Model: Chapter 3

 

Classical Normal Linear Regression Model: Chapter 4

 

Week 3:  Jan 30, Feb 1

 

Classical Normal Linear Regression Model: Chapter 4

 

Review

 

 

Week 4:  Feb 6, 8

 

First Exam, Feb 6, Chapters 1,2,3 and 4

 

Interval Estimation and Hypothesis Testing: Chapter 5

 

Week 5:  Feb 13, 15

 

Interval Estimation and Hypothesis Testing: Chapter 5

 

Extension of the Two-Variable Linear Regression Model: Chapter 6

 

Week 6:  Feb 20, 22

 

Multiple Regression  Analysis Chapters 7 and 8

 

Week 7:  Feb 27, 29

 

Review

 

Second Exam, Feb 29: Chapter 5,6,7,8

 

Week 8:  Mar 5, 7

 

Dummy variables: Chapter 9

 

Multicollinearity: Chapter 10

 

Week 9:  Mar 12, 14

 

Multicollinearity: Chapter 10

 

Heteroscedasticity: Chapter 11

 

Week 10:  Mar 26, 28

 

Heteroscedasticity: Chapter 11

 

Week 11:  Apr 2, 4

 

Review

 

Third Exam, Apr 7

 

Week 12:  Apr 11

 

Autocorrelation Chapter 12

 

Week 13:  Apr 16, 18

 

Qualitative Response Regression Models: Chapter 15

 

Week 14:  Apr 23

 

Time series econometrics, Basic Concepts: Chapter 21

 

Review

 

Project due: April 23.

 

 

Final Exam: Comprehensive Exam (see University schedule for date and time)