University of Hong Kong School of Economics and Finance

ECON0701: Introductory Econometrics Spring 2014

I. INFORMATION ON INSTRUCTOR AND TUTOR Instructor: James P. Vere, 901 K. K. Leung Building Office Hours: Tuesday 2 pm - 3 pm. Phone: x3040 Email: jpvere@econ.hku.hk Teaching Assistant: Luyao PAN (h0996109@hku.hk) II. COURSE DESCRIPTION AND OBJECTIVES Course description: Most of economics is modeling relations among economic variables. Examples range from the relation between interest rates and inflation rate, the effect of the education level on income, or the relationship between prison sentences and crime rates. To evaluate these relations, economists rely on data analysis. Econometrics, hence, is this branch of economics that formulates statistical methodology to deal with the empirical problems typical of economic data. Consequently, the objective of this course is to prepare students for basic empirical work in economics. In particular, topics will include basic data analysis, regression analysis, and hypothesis testing. Students will be provided with the opportunity to use actual economic data to test economic theories. Course objectives:

1. To acquire and internalize knowledge of statistical methods used by economists and financial professionals

2. To develop the ability to discern which method is most appropriate in a given situation, and understand the limitations of the chosen method

3. To acquire the skills to apply these methods in a variety of contexts (e.g. microeconomic analysis, macroeconomic analysis, and policy analysis) and with a variety of instruments (e.g. Stata, Excel, calculator and statistical table)

Textbook Wooldridge, Jeffrey M (2009). Introductory Econometrics. 4th edition. Singapore: Cengage

Learning.

III. LEARNING OUTCOMES By the end of this course, students should be able to:

1. Understand the basic finite-sample properties of estimators (e.g. unbiasedness, efficiency) and the conditions under which they apply

2. State and understand the Gauss-Markov theorem 3. Understand the basic large-sample properties of estimators (e.g. consistency, asymptotic

efficiency) and the conditions under which they apply 4. Use Stata to calculate the coefficients of the multiple linear regression model 5. Intrepret the coefficients of the multiple linear regression model (in both cross-sectional and

time-series settings) 6. Calculate and interpret the R2 measure of model fit 7. Perform t-tests of single linear hypotheses and F-tests of joint linear hypotheses (with Stata

and by hand) 8. Understand the implications for estimation results when assumptions of the classical linear

model are violated (e.g., omitted variables, heteroskedasticity, serial correlation) 9. Test for violations of the assumptions of the classical linear model (with Stata; where

appropriate) 10. Know intuitively when the assumptions of the classical linear model would be inappropriate in

a real-world setting 11. Specify and use Stata to estimate weighted least squares regressions that correct for the

problem of heteroskedasticity 12. Specify and use Stata to estimate Prais-Winsten regressions that correct for the problem of

serial correlation IV. ALIGNMENT OF PROGRAM AND COURSE OUTCOMES The following matrix indicates the alignment between the course learning outcomes and the program learning outcomes. Program Learning Outcome Associated Course Learning Outcomes

Know the fundamental principles and theories of economics and finance

1-3, 8

Be able to use analytical tools to formulate and solve economic and business problems

4, 6, 7, 9

Be able to distinguish between minor and major issues

8, 10

Be able to identify and use relevant information

5, 6, 8, 10

V. TEACHING AND LEARNING ACTIVITIES Teaching and learning takes place through lectures, tutorials, and assignments. VI. ASSESSMENT Your final grade in this class will depend on three things: your homework (15%), a midterm examination (25%), and your final examination grade (60%). There will be a number of problem sets (approximately one per week) which will count toward 15% of your final grade. These problems will be assigned on Fridays to be handed in by the following Friday. You may work with other students on the problem sets, but you must submit your own answers. The understanding of the homework problems is a necessary condition for understanding the course. Hence, if you do not spend enough time on the homework, your understanding of the material will be poor, and so too will be your grade. The midterm exam will take place in class on Monday, March 17th. The final exam will be comprehensive. Both the midterm and the final are mandatory.

VII. STANDARDS FOR ASSESSMENT The basis for assessment is a weighted numerical average of students’ homework, midterm and final examination scores. Letter grades will be assigned in accordance with Faculty-level guidelines for courses offered by the School of Business and the School of Economics and Finance. VIII. ACADEMIC CONDUCT

Plagiarism and copying of copyright materials are serious offenses and may lead to disciplinary actions. You should read the chapters on “Plagiarism” and “Copyright” un the Undergraduate/Postgraduate Handbook for details. You are strongly advised to read the booklet entitled “What is Plagiarism?” which was distributed to you upon your admission to the University, a copy of which can be found at http://www.hku.hk/plagiarism. A booklet entitled “Plagiarism and How to Avoid It” is also available from the Main Library.

When completing homework assignments, you are permitted to consult with classmates and the tutor but you must do the assignment individually and hand in your own work.

Consulting previous semesters’ answer keys or referring to any examination papers not available on the course Web page or in the Library’s ExamBase database are strictly prohibited.

Academic violations will result in automatic failure of the course, and may result in further disciplinary action, up to and including discontinuation of studies.

IX. COURSE SCHEDULE Tentative and subject to change. Refer to problem sets on the course Web page for definitive due dates. Week of 20 January Review of Probability and Statistics Wooldridge Appendices A, B and C Week of 27 January The Simple Regression Model Wooldridge Chapter 2 Week of 3 February Chinese New Year (no class) Week of 10 February Multiple Regression Analysis: Estimation Wooldridge Chapter 3 Problem Set 1 due Week of 17 February Mutiple Regression Analysis: Inference Wooldridge Chapter 4 Problem Set 2 due Week of 24 February Multiple Regression Analysis: Inference (continued) Wooldridge Chapter 4 Problem Set 3 due Week of 3 March Regression Analysis with Qualitative Information Wooldridge Chapter 7 Problem Set 4 due Week of 10 March Reading Week Week of 17 March MIDTERM EXAMINATION Week of 24 March Multiple Regression Analysis: OLS Asymptotics Wooldridge Chapter 5 Week of 31 March Heteroskedasticity Wooldridge Chapter 8 Problem Set 5 due Week of 7 April Basic Regression Analysis with Time Series Data Wooldridge Chapter 10 Problem Set 6 due Week of 14 April Further Issues in Using OLS with Time Series Data Wooldridge Chapter 11 Problem Set 7 due Week of 21 April Serial Correlation and Heteroskedasticity in Time Series Regressions Wooldridge Chapter 12 Problem Set 8 due Week of 28 April Serial Correlation and Heteroskedasticity in Time Series Regressions

(continued) -or- special topic (time permitting) Problem Set 9 due