January 9, 2006

 

 

Econometrics 1 (ECO 7424)

Ref. No. 11192

Department of Economics, Florida International University (University Park)

Spring Semester 2006

 

 

Instructor:  Prasad Bidarkota                                       

Office: DM 320A         Tel: (305) 348-6362

E-mail: bidarkot[at]fiu.edu

Web Address: http://www.fiu.edu/~bidarkot/

Office Hours:  Tue & Thurs 5:15-6:15pm and by appointment

Lectures: Tue & Thurs 3:30-4:45pm in GL 137

 

 

 

Textbook

William H. Greene (2003), Econometric Analysis, 5th Edition, Prentice Hall.

 

For Reference:

            William E. Griffiths, R. Carter Hill, and George G. Judge (1993),

Learning and Practicing Econometrics, John Wiley & Sons, Inc.

G.G. Judge, W.E. Griffiths, R.C. Hill, H. Lutkepohl, and T-C. Lee (1993),

An Introduction to the Theory and Practice of Econometrics,

John Wiley & Sons, Inc.

G.G. Judge, W.E. Griffiths, R.C. Hill, H. Lutkepohl, and T-C. Lee (1985),

The Theory and Practice of Econometrics, 2nd Edition, John Wiley &

Sons, Inc.

Russell Davidson and James G. MacKinnon (2004), Econometric Theory and

Methods, Oxford University Press.

Andrew C. Harvey (1991), An Econometric Analysis of Time Series, 2nd Edition,

Cambridge University Press.

Maddala, G.S. (1992), Introduction to Econometrics, 3rd Edition, Prentice Hall.

 

 

           

Reference for Applications in Economics and Finance:

            Ernst R. Berndt (1996), The Practice of Econometrics: Classic and Contemporary,

Addison-Wesley Publishing Company.

 

 

Course Objectives

            The course has two objectives. The first is to introduce some basic topics in econometrics. Regular homework assignments will be given to enhance understanding of the core material in the course. The second objective is to get students familiar with the art of conducting empirical work in econometrics through the use of suitable computational software. Towards this end, computer assignments will be given periodically throughout the course. Students are required to work with the GAUSS software for their homework assignments.

 

 

Assessment

The course assessment will consist of several homework and computer assignments together worth 30%, a midterm examination worth 30%, and a final exam worth 40%.

The midterm will be held roughly halfway through the semester at a date, time, and location to be determined later.

 

 

Guidelines for Submitting Homework and Computer Assignments

            Homework and computer assignments will be given throughout the semester on all major topics covered in the course (see below under course outline). A total of five assignments will be given in the course. Each will consist of several questions, analytical and computational, frequently from the back of the chapters in the textbook. Students are responsible for answering all the questions assigned for each homework.

            Students are encouraged to work in collaboration with a partner on their homework and computer assignments. Only one copy of the homework / computer assignment is to be handed in between every two students.

            Although I do not expect typed homework submissions, these nevertheless have to be neatly written, stapled, concise yet complete, and include all relevant computer programs and computer output where appropriate.

            Students need to submit the computer code written for their homework electronically by e-mail as well.

            Solutions to the homework questions will be discussed in class.

Late assignments will not be accepted for any reason whatsoever.

 

 

Makeup Examination

            There will be no makeup examination under any circumstances.

 

 

Grades

            The final course grade will be based on the cumulative total score in the course comprising of the scores on the homework and computer assignments, midterm, and the final exam. Letter grades will be based on the distribution (“curve”) of these final scores of all students in the course. Depending on the overall performance of the students, the minimum total score required to obtain a particular grade (“the cutoff”) will be determined at the end of the semester.

 

 

Course Outline

 

  1. The General Linear Statistical Model (GLSM). Chapter 2.
    1. Specification of the GLSM
    2. Matrix algebra
    3. GAUSS program; GAUSS lab

 

Homework Assignment 1:

Questions:

Due Date: 

 

 

  1. Least Squares. Chapter 3.
    1. Estimation of the GLSM by Least Squares (LS)
    2. LS as projection in vector spaces; Projection matrix; Orthogonal vectors
    3. Frisch-Waugh theorem; Estimating a subset of the parameter vector; Orthogonal regression
    4. Goodness-of-fit measures; ,
    5. Estimating ; Unbiasedness of LS estimator; Variance of the LS estimator; Gauss-Markov theorem

 

Homework Assignment 2:

Questions:

Due Date: 

 

 

  1. Inference in the General Linear Model under Normality. Chapter 4.
    1. Distribution of the LS estimator; Distribution of the LS residual
    2. Exact tests under normality; the t-test; the F-test

 

Homework Assignment 3:

Questions:

Due Date: 

 

 

  1. Large-Sample Properties of the Least Squares Estimators. Chapter 5.
    1. Asymptotic theory
    2. Modes of convergence; Convergence in Probability; Convergence in Distribution; Slutsky Theorem; Continuous Mapping Theorem
    3. Law of Large Numbers; Central Limit Theorem; A Useful Central Limit Theorem
    4. Asymptotic Distribution of LS estimators; Asymptotic Distribution of t- and F-test statistics
    5. Large Sample Distributional Properties – Consistency, Asymptotic Unbiasedness, and Asymptotic Efficiency Consistency; Asymptotic Normality; Asymptotic Efficiency

 

Homework Assignment 4:

Questions:

Due Date: 

 

 

  1. Specification Analysis and Model Selection. Chapter 8.
    1. Multicollinearity;
    2. Variable specification errors; Over- and Under-parameterizations (Omission of Relevant Variables and Inclusion of Irrelevant Variables)
    3. Consequences on unbiasedness, efficiency, and predictions
    4. Testing Non-Nested Hypotheses
    5. Model Selection Criteria; , AIC, SBC

 

Homework Assignment 5:

Questions:

Due Date: 

 

 

  1. Models with Random Regressors. Chapter 5.
    1. Random Regressors & Errors in Variables Models; Problems with OLS Estimator; Forward and Reverse Regressions; Obtaining Consistent Bounds by OLS
    2. Estimation – Method of Moments, Instrumental Variables Method, Two Stage Least Squares
    3. Where do Instruments come from? What to do when we have more Instruments than we need?
    4. Properties of IV Estimator; Testing for Correlation Between a Regressor and the Error – Hausman test

 

 

  1. Generalized Least Squares (GLS). Chapter 10.
    1. Non-Scalar Identity Covariance Matrix
    2. Covariance Matrix under Pure Heteroskedasticity; Weighed Least Squares (WLS) – a Special Case of GLS
    3. Feasible and Infeasible GLS
    4. Distribution of Feasible and Infeasible GLS Estimators; The t- and F- test Statistics