Syllabus / reading list - Ka-fu Wong - The University of Hong Kong

THE UNIVERSITY OF HONG KONGFACULTY OF BUSINESS AND ECONOMICSSchool of Economics and FinanceECON6037: Economic ForecastingSpring 2009Instructor: Ka-fu WONGPhone number: 2857-8512.Office: KKL 1002.Office hours: By appointmentEmail: kafuwong@econ.hku.hkCourse webpage: http://kafuwong.econ.hku.hk/teaching/econ6037/2009S/COURSE INFORMATION:Prerequisites:Basic training in introductory statistics at undergraduate level or equivalent is assumed.Course OverviewThe determination of costs and benefits of alternative policies often requires forecasts of several keyvariables. Also, ex-post evaluation of a policy requires the understanding of the seasonal and cyclicalfluctuations in the target variable, so as to distinguish the effects of policy from these periodicfluctuations. This course provides an overview of forecasting methods.This course introduces the elements of forecasting and various forecasting techniques. The methodsinclude regression based models, ARIMA models, exponential smoothing methods and multivariatemethods. The course also looks at techniques for the evaluation of performance of forecastingmethods and examines the role of forecasts in the decision making process.Course Objectives1. To provide a thorough understanding of basic forecasting models.2. To develop a hands-on knowledge of economic forecasting.Textbook + SoftwareWong, Ka-fu (2009): Economic Forecasting (a preliminary draft in preparation, to be made availablechapter by chapter in our class website).Students will be required to use E-Views, a leading econometric modeling and forecast program. Theexamples in the textbook were generated using E-Views. To take full advantage of the software inforecasting, we will need a full version of the software, available at the School computer lab at KKL1009. Sample programmes will be provided to lower the cost of learning the software.An additional reference will be Diebold, Francis X. (2006): Elements of Forecasting, 4th edition,South-Western.-1 -

INTENDED LEARNING OUTCOMESOn completion of this course, students should be able to:CILO1. Collect data, select the appropriate model to produce out-of-sample forecast, and write ashort report based on the results.CILO2. Understand and evaluate basic forecasting results of research reports.ALIGNMENTS OF PROGRAM AND COURSE LEARNING OUTCOMES:Program Intended Learning Objectives1. To master current theory and knowledge inthe field of economics2. To master the skills to deal with economicdata.3. To develop the ability to apply economics toreal world problems.4. To develop global and regional outlook andastutenessCourse Intended Learning Objectives(CILOs)CILO1, CILO2CILO1, CILO2CILO1, CILO2CILO1TEACHING AND LEARNING ACTIVITIESTLA1.TLA2.TLA3.Lectures:Instructor will give lectures on major concepts and issues.Homework:Students will be asked to produce forecast using different datasets and forecastingtools covered.Consultation:The instructor holds weekly consultation hours to address students’ questions.Students are also encouraged to discuss questions with the instructor by email or aforum in the class website.ASSESSMENTAssessmentWeightsA1. Homework assignments 50%A2. Project 50%A3. Forecast competition (bonus, maximum 20%)Homework assignments: Homework assignments will be given on an approximately bi-weekly basisand these will account for approximately 50% of students’ grade. The aim is to let students practice thebasic elements of forecasting. Each student will have to work on a different economic time series fromthe other students. Students will have to upload a Powerpoint file (along with the Eviews work file andprogram) summarizing the findings.The homework assignments will be assessed on the following aspects:(1) Statistical Analysis: 60%(2) Clarity (Readability): 40%-2 -

Project: The aim is to get student to practice the preparation of a complete forecast report for thegovernment or a private firm. A formal written report has to be submitted within one week after theend of the course. The written report will be worth 50% of your grade.Students can choose any project, subject to the approval of the instructor. One excellent possibility isto work on an actual forecasting project with a local business. Another possibility is to forecast thestock performance of listing firms. All projects must have an Asian context (preferably Hong Kong, Macau,Taiwan or China).The paper (text, graphs and tables inclusive) is restricted to a maximum of 15 pages, double-line spacedwith Times Roman 12 pts, and margins of 1 inch from all sides. A topic should be submitted to“project topic wiki” starting 10:00p.m. Friday, March 14. The paper (along with the Eviews workfileand program) is due at 10:00p.m. Friday, May 2, both via the course website.To guard against plagiarism, all submitted papers will be scanned by Turnitin.The term paper will be assessed on the following aspects:(1) Abstract: 5%(2) Motivation/Objective: 10%(3) Statistical Analysis: 40%(4) Clarity (Readability): 30%(5) Citation/Reference: 10%(6) Format Conformity (page margin, line spacing, font size, etc.): 5%Forecast competition (Bonus, maximum 20%): Students are inivted to forecast the weekly closingof Hang Seng Index. All competitors have to submit their forecasts on Sunday for the followingweek’s closing. It has to be submitted to the course website with a brief discussion on how theforecast was obtained. The instructor will also produce a forecast as a benchmark. The n students whoconsistently perform better than the instructor will share the bonus equally, i.e., each gets 20/n bonuspoints. More will be discussed on the first day of class.Relationship Among Intended Learning Outcome, Teaching And Learning Activities AndAssessmentsCILO Teaching and Learning Activities (TLA) Assessments (A)1 2 3 1 2 31✓ ✓ ✓ ✓ ✓ ✓2✓ ✓ ✓ ✓ ✓ ✓STANDARDS OF ASSESSMENTOverall grades are given using the following criteria:Grade PerformanceA+, A, A- > 80B+, B, B- 65 - 80C+, C, C- 50 - 65D+, D 40 - 50F < 40-3 -

ACADEMIC CONDUCTThe University Regulations on academic dishonesty will be strictly enforced! Please check theUniversity Statement on plagiarism on the web: http://www.hku.hk/plagiarism/Academic dishonesty is behavior in which a deliberately fraudulent misrepresentation is employed in anattempt to gain undeserved intellectual credit, either for oneself or for another. It includes, but is notnecessarily limited to, the following types of cases:a. Plagiarism - The representation of someone else’s ideas as if they are one’s own. Where thearguments, data, designs, etc., of someone else are being used in a paper, report, oralpresentation, or similar academic project, this fact must be made explicitly clear by citing theappropriate references. The references must fully indicate the extent to which any parts of theproject are not one's own work. Paraphrasing of someone else's ideas is still using someoneelse’s ideas, and must be acknowledged.b. Unauthorized Collaboration on Out-of-Class Projects - The representation of work as solelyone's own when in fact it is the result of a joint effort.Where a candidate for a degree or other award uses the work of another person or persons withoutdue acknowledgement:1. The relevant Board of Examiners may impose a penalty in relation to the seriousness of theoffence;2. The relevant Board of Examiners may report the candidate to the Senate, where there is primafacie evidence of an intention to deceive and where sanctions beyond those in (1) might beinvoked.CLASS SCHEDULEWeek Topic1 The Basic Issues2 Modeling and Forecasting TrendModeling and Forecasting Seasonality3-4 Characterizing Cycles5-6 Modeling Cycles: MA, AR, and ARMA Models7 Forecasting Cycles8 A Forecasting model with Trend, Seasonal, and Cyclical Components9 Forecasting with Regression Models10 Evaluating and Combining Forecasts11 Unit Roots, Stochastic Trends, ARIMA Forecasting Models, and Smoothing12 Volatility Measurement, Modeling and Forecasting-4 -