CATALOGUE DESCRIPTION

This course is about application of statistical tools, especially regression analysis, for estimating economic relationships and testing economic hypotheses. Econometrics is the study of relationships among economic variables, primarily through the use of computer-calculated regression equations. This half-semester, Master's-level course is designed to give students who has some familiarity with statistics a basic introduction to techniques of regression analysis used in economics, business, and finance. Within the short duration of this class, issues of specification, interpretation, and evaluation of econometric models will be emphasized, in the context of hands-on practical exercises and using state-of-the-art computer resources.

AIMS & OBJECTIVES

This course aims at giving students basic understanding of econometrics theories and applying econometric techniques of regression analysis. Numerous applications are examined to achieve this goal. Emphasis is placed on the classical linear regression model, least-squares estimation, hypothesis testing, and modeling building. Various econometric models are adopted to analyze practical economic problems and make forecasts. Furthermore, in this course students are trained to use computer statistics software (i.e., EViews).

To achieve the course objectives I will: (1) explain some of the theory behind estimating economic models, and (2) provide you with experience applying basic statistics and econometrics using micro-computers. I want you to leave this course with skills that are highly valued and actually utilized in business and government decision-making. Fulfillment of both objectives (1) and (2) are necessary before one can effectively apply econometrics.
 

GENERAL LEARNING OUTCOMES (COMPETENCES)

 On successful completion of this course, all students will have developed knowledge and understanding of:

-       Simple and multiple regression
-       Least squares and maximum likelihood
-       Statistical inference in regression models
-       Restriction testing
-       Prediction
-       Heteroscedasticity
-       Autocorrelation
-       Multicollinearity
-       Dummy variables 

On successful completion of this course, all students will have developed their skills in:

-       run and interpret the results of multiple regression
-       test a multiple regression for mis-specification and parameter restrictions
-       adapt the regression process for various departures from classical conditions
-       apply various procedures for choosing between models
-       add appropriate dummy variables to reflect shifts and kinks in relationships
-       EViews programming

On successful completion of this course, all students will have developed their appreciation of and respect for values and attitudes regarding the issues of:

-       Applied economic modeling
-       Using computer software in econometric modeling
-       Using statistical inference in econometric model building, estimating, and testing

GRADING CRITERIA

1. All of the homework and lab assignments (20 percent)

2. A medium length “term paper” on a topic approved by me (20 percent)

3. A midterm exam (25 percent)

4. A final exam (35 percent)

 RELATIONSHIP WITH OTHER COURSES

Builds on ECON 310 (Introductory Econometrics – undergraduate level) + ECON315 (Mathematical Economics – undergraduate level)

 LEARNING / TEACHING METHOD

Blackboard demonstrations; slideshow presentations; computer demonstrations; overhead projections; classroom discussions; Q and A sessions; individual coaching.

 Even more than ECON 310, this is a hands-on course: you will learn econometrics by doing it. The EViews software does almost all the econometric calculations, so most of what you have to do as an econometrician is be sure you understand what instructions to give and then how to interpret its output. Your textbook and the excellent user's manual for the software both explain all the concepts we will use. Lecture periods will be used to explain some concepts, to provide some further illustrations and applications, and to coach you in the process of application. You will have further applications in  written assignments for credit, a mid-term test to give you feedback on your understanding, and a three hour final exam (with software, in the computer lab) to show your total mastery of the field.

 ASSIGNMENTS

 There will be several assignments where you need to solve problems relating to the topics we covered in class. I will also assign computer lab exercises. Lab reports must be handed in for the computer exercises. Failure to submit lab reports on a regular basis will result in F for the course.

 METHOD OF ASSESSMENT

 Credit for this course is obtained by successfully completing

1. All of the homework and lab assignments (20 percent)

2. A medium length “term paper” on a topic approved by me (20 percent)

3. A midterm exam (25 percent)

4. A final exam (35 percent)

 ATTENDANCE

 By EMU regulations, attendance is compulsory. Failure to attend more than 5% of classes without legitimate excuses will result in a grade of NG.

 TEXTBOOK/S

[1] Maddala, G.S., 2001, Introduction to Econometrics, 3/e, Wiley  (M)
[2] Gujarati, D. N., 2004, Basic Econometrics, 4/e., McGraw-Hill (G)

 INDICATIVE BASIC READING LIST

[1]  Hill, Griffiths & Judge, 2001, Undergraduate Econometrics, 2/e, Wiley.
[2]  Maddala, G.S., 2001, Introduction to Econometrics , 3rd Edition, Wiley
[3]  Pindyck & Rubinfeld, 1997, Econometric Models and Economic Forecasts, 4/e., McGraw-Hill.
[4]  Ramanathan, Ramu, 2001, Introductory Econometrics with Applications, 4/e., Dryden.
[5]  Stock, J.H. and M.W. Watson, 2003, Introduction to Econometrics, Addison-Wesley.
[6]  Studenmund, A.H., 2001, Using Econometrics: A Practical Guide, 4/e., Addison-Wesley-Longman.
[7]  Verbeek, M, 2000, A guide to Modern Econometrics, Wiley.
[8]  Wooldridge, Jeffery. M., 2003, Introductory Econometrics: A Modern Approach, 2/e., Thomson-South-Western.
[9] Lecture notes - see the listing on the web page.

 EXTENDED READING LIST

Our major textbook is Maddala’s Introduction to Econometrics. When a more illustrative treatment of the topic is needed with more examples I recommend reading the Gujarati’s Basic Econometrics.

1. Introduction
     1.1 The Methodology of Econometrics
     1.2 The Nature of Regression Analysis?
     1.3 Review of Basic Mathematical statistics and Probability Concepts
     1.4 Understand the Structure of Economic Data

Maddala, Chapter 1, 2 (Sections: 1.1-1.3, 2.6-2.9)   
Gujarati, Chapter 1, 2, and Appendix A

2. The Ordinary Least Squares Method
     2.1 The Theoretical and Estimating Empirical Regression model
     2.2 The Significance of the Stochastic Disturbance Term
     2.3 The Method of Ordinary Least Squares

 Maddala, Chapter 3 (Sections: 3.1-3.3) 
Gujarati, Chapter 3

3. The Classical Regression model : Estimation, Properties and Inference
     3.1 The Classical Assumptions of the OLS estimators
     3.2 The Normality Assumption of the Stochastic Error Term
     3.3 The Sample Distribution of the OLS Estimators 
     3.4 The Gauss-Markov theorem
     3.5 The Coefficients of Determination and Standard econometric notations

Maddala, Chapter 3 (Sections: 3.4, Appendix)
Gujarati, Chapter 4

4. Hypothesis Testing
     4.1 What is hypothesis testing?
     4.2 Confidence Intervals for Regression coefficients
     4.3 Testing the Significance of Regression Coefficients
     4.4 Prediction and Residual analysis
     4.5 Applications of the Regression Analysis

Maddala, Chapter 3 (Sections: 3.5-3.7) 
Gujarati,  Chapter 5

5. Extensions of the Simple Regression Model
     5.1 Regression Through the Original
     5.2 Regression on Standardized Variables
     5.3 Function Forms of Regression Models

Maddala, Chapter 3 (Sections: 3.8-3.9)
Gujarati,  Chapter 6

6. The Linear Multiple Regression Model: Estimation
     6.1 The Classical Linear Multiple Regression Model: Interpretations and Assumptions
     6.2 OLS and the Properties of Regression Coefficients
     6.3 The Goodness of Fit of the Multiple Regression Model
     6.4 The Scaling problem

Maddala, Chapter 4 (Sections: 4.1-4.7)
Gujarati, Chapter 7

7. The Linear Multiple Regression Model: Hypothesis Testing
    7.1 Testing the Significance of Individual coefficient and Overall of the Regression Model
    7.2 Testing Linear Equality Restrictions: The Partial Restriction Tests
    7.3 Testing for Structural or Parameter Stability: The Chow Test
    7.4 Testing the Functional Form of Regression 

Maddala, Chapter 4 (Sections: 4.8-4.11)
Gujarati, Chapter 8

8. Dummy Variables
     8.1 Regression on a single dummy independent variable
     8.2 Regression on dummy variables with many categories
     8.3 Interactions involving dummy variables
     8.4 Testing the equivalence of various regressions

Maddala, Chapter 8 (Sections: 8.1-4.3,8.5)
Gujarati, Chapter 9

9. Multicollinearity
     9.1 The nature of multicollinearity
     9.2 Consequences of ignoring multicollinearity
     9.3 Detections of multicollinearity
     9.4 Remedial measures

Maddala, Chapter 7
Gujarati,  Chapter 10

10. Heteroscedasticity
     10.1 The nature of Heteroscedasticity
     10.2 Consequences of Ignoring heteroscedasticity
     10.3 Detection of Heteroscedasticity
     10.4 Remedial Measures

Maddala, Chapter 5
Gujarati,  Chapter 11

11. Serial-correlation (Autocorrelation)
       11.1 The Nature of Time Series Autocorrelation
       11.2 Consequences of Ignoring Autocorrelation
       11.3 Testing for Autocorrelation
       11.4 Remedial Measures

Maddala, Chapter 5
Gujarati, Chapter 12

SEMESTER OFFERED

 2008-2009 Fall Semester

CONTENTS & SCHEDULE

 Lectures will be held on Tuesday (4:30-7:30 pm) in RD 101. The lecture topics within the semester are as in the following schedule:

 PLAGIARISM

 This is intentionally failing to give credit to sources used in writing regardless of whether they are published or unpublished. Plagiarism is a disciplinary offence and will be dealt with accordingly.