Objective : This subject aims at giving students basic understanding of econometrics theories and applying econometric techniques to specific empirical economic problems. Numerous examples are examined to achieve this goal. Emphasis is placed on the classical linear model, least-squares estimation, hypothesis testing, and model building. Econometric models are then adopted to analyze practical problems and make forecasts. Furthermore, students are trained in this subject to use computer statistics software. (i.e., EVIEWS).

Textbook : Studenmund, A.H., 2006, Using Econometrics: A Practical Guide, 5/e., Pearson-Addison-Wesley
                  

References:

Hill, Griffiths & Judge, 1998, Undergraduate Econometrics, Wiley.
Maddala, G.S., 2001, Introduction to Econometrics , 3rd Edition, Wiley
Pindyck & Rubinfeld, 1997, Econometric Models and Economic Forecasts, 4/e., McGraw-Hill.
Ramanathan, Ramu, 2001, Introductory Econometrics with Applications, 4/e., Dryden.
Stock, J.H. and M.W. Watson, 2003, Introduction to Econometrics, Addison-Wesley.
Gujarati, D. N., 2003, Basic Econometrics, 4/e., McGraw-Hill 
Verbeek, M, 2000, A guide to Modern Econometrics, Wiley.
Wooldridge, Jeffery. M., 2003, Introductory Econometrics: A Modern Approach, 2/e., Thomson-South-Western.

Course Outline:

1. Introduction: (Week 1)
     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

    Studenmund (2006) (Chapter: 1 and **Appendix)

2. The Regression Model and the Ordinary Least Squares Method: (Week 2)
     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

    Studenmund (2006) (Chapters 2)

                                       Quiz 1: 28th January 2008

3. The Classical Regression model : Estimation, Properties and Inference (Week 3)
     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

    Studenmund (2006)  (Chapter 3)

4. Hypothesis Testing - Single simple hypothesis test (Week 4)
     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

    Studenmund (2006)  (Chapter 4)

                                Quiz 2: (18th February 2008 change to 25th February 2008)

5. Hypothesis Testing -  Joint and Compound Hypotheses (Week 5)
     5.1 The Goodness of Fit of the Multiple Regression Model
     5.2 Regression on Standardized Variables
     5.3 Function Forms of Regression Models
     5.4 Applications of the Regression Analysis

    Studenmund (2006)  (Chapter 5)

6. Extension of The Linear Regression Model (Week 6)
     6.1 The CLMR Model: Interpretations and Assumptions
     6.2 OLS and the Properties of Regression Coefficients
     6.3 Regression Through the Original
     6.4 The Measure Scaling problem

    Studenmund (2006)  (Chapter 5)

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(Mid-term examination will be scheduled on Week 7th (Tentative date: 3rd March 2008 )
Examination Venue: WLB702  (2 hours: 2:30~4:30pm)

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7. The Restriction Effect, Functional Form and Specification (Week 8)
    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 

  Studenmund (2006)   (Chapter 6)

8. Dummy Variables (Week 9)
     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

   Studenmund (2006)  (Chapter 7)

                                Quiz 3: 31st March 2008  

9. Multicollinearity (Week 10)
     9.1 The nature of multicollinearity
     9.2 Consequences of ignoring multicollinearity
     9.3 Detections of multicollinearity
     9.4 Remedial measures

    Studenmund (2006)  (Chapter 8)

10. Serial-correlation (Autocorrelation) (Week 10-11) 
       11.1 The Nature of Time Series Autocorrelation
       11.2 Consequences of Ignoring Autocorrelation
       11.3 Testing for Autocorrelation
       11.4 Remedial Measures

    Studenmund (2006)  (Chapter 9)

                                Quiz 4: 14th April 2008

11. Heteroscedasticity (Week 11-12)
     10.1 The nature of Heteroscedasticity
     10.2 Consequences of Ignoring heteroscedasticity
     10.3 Detection of Heteroscedasticity
     10.4 Remedial Measures

    Studenmund (2006)   (Chapter 10)

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