Spring 2005:
Syllabus
Instructor: Professor Barry Keating
Office: 226 Mendoza College of Business
Business Forecasting (FIN 480/602/602a)
Syllabus -- Spring 2005: Jump to Assignment Sheet directly.
Textbook: J. Holton Wilson and Barry Keating. Business Forecasting, Fourth Edition
(McGraw Hill/Irwin, 2001) ISBN 0-07-252646-7
ForecastX For Excel (statistical software available in the Notre Dame Campus Clusters and the London computing cluster - also included with the textbook)
A calculator can be a valuable resource in this course (especially for exams). You are allowed to use a calculator on the exams. The most useful calculators for this course are programmable calculators.
Forecasts may be either subjective or objective. A subjective forecast can be prepared by reading extensively about a situation and the economy, and then combining this information through some unspecified judgement process to come up with a forecast. A disadvantage of this form of forecasting is that there is no systematic way to improve forecast accuracy by learning "correct" techniques.
The objective approach to forecasting, on the other hand, involves developing a model which is generally constructed by studying past relationships between the item to be forecast and the factors thought to affect it. Objective forecasting methods have several advantages over the subjective variety. Because they are objective, the forecasts are not affected by what the forecaster wishes the outcome to be. Many of the objective methods also include processes by which the forecasting model learns from its past errors. Perhaps most importantly, objective methods provide a basis for evaluating forecast accuracy and for developing confidence ranges for forecasts. This course concentrates on these objective methods of forecasting.
Economic forecasting in general, and this course in particular, are designed to explain the nature of the real world; the intent here is to integrate theory and application. Theory is only justified by its power of application in this course.
All forecasting problems can be divided into three types. The first type involves forecasting the amount of something, e.g., sales, customers served, birth rates, or stock prices. The second type of forecast involves the timing of some event, such as the date on which a machine part will fail. The third type of forecast involves the probability of some events occurring, such as the probability of rain on July 15 of next year. This course will concentrate on the first of these types of forecasts -- forecasts of amounts. These are the most common of forecasting problems encountered in business. There is a certification process available to forecasters much like the Certified Financial Analyst designation or the Certified Professional Accountant designation. The Certified Professional Forecaster designation is available through the Institute for Business Forecasting.
Regular attendance is essential to the successful completion of this course. Attendance will regularly be taken and you are responsible for material covered in class whether or not you have attended class. Missing more than three class sessions (for any reason) will result in an automatic reduction in course grade. Unsatisfactory attendance may result in a failing grade. You should plan on spending at least two hours of independent study for each hour of class attendance.
A course grade will be assigned on the basis of student performance on two examinations, a final examination, assignments, and textbook problems. The assignments and textbook problems will be presented in class.
Assignments/Problems/Class Participation 35%
First Midterm Exam 20%
Second Midterm Exam 20%
Final (comprehensive) Exam 25%
On the attached "assignment sheet" you will find a class-by-class list of topics to be covered and your reading assignment. Reading assignments in the textbook are to be completed before the class day under which they are listed in the assignment sheet. Problem assignments are to be completed on the date listed and the solutions will be presented by selected students to the class on transparencies for projection (with the transparencies presented to the instructor immediately after the presentation).
Assignments (essentially longer problems, directed exercises, or reviews of articles presented in class) will be assigned for most of the topics covered and will be presented by students in class. The class presentation of assignments and textbook problems (using transparencies) is an important and integral part of the course.
In Class presentations will be evaluated according to a rubric.
Each of these examinations will be a full-period examination of essentially a problem-solving nature; problems will be similar to those in the textbook. Because of the technical nature of these examinations, students are allowed to use calculators. These examinations, however, are to be completed without reference to the textbook, class notes or any other materials. The tests may also include a practicum using the econometric modeling software assigned for class use.
A comprehensive final examination will be administered during the "final examination period" of the university at the Registrar's selected time and date:
Monday May 2, 2005 for the all sections in DeBartolo 138 (see below).
Note: The 2005 undergraduate students will not have a project!
The following applies to students registered for Fin 602 or Fin 602a.
This is both a final exercise for the course and an ongoing effort throughout the semester. The project involves gathering and analyzing data. The project will be summarized in a multimedia display for class presentation late in the semester; a written report will also be required. Complete guidelines for the project are available on the homepage under term projects.
Assignments turned in late (that is, after the class meeting time on the assigned due date), without a valid University approved excuse, will receive a grade of zero.
Class# Date Topic Assignment
1 1/12 Introduction to Business Forecasting and overview of the ForecastX computing package--Chapter 1
2 1/17 Introduction continued --
problems c1p2 and c1p3 assigned to ___
problem c1p5 assigned to __
3 1/19 IThe Forecast Process, Data Considerations, and Model Selection --Chapter 2 --
4 1/24 The Forecast Process, Data Considerations, and Model Selection --Chapter 2 (continued)
Case 1 (Random Walk Down Wall Street) assigned to __
Case 2 (Trend in Time Series Data) assigned to ____
problems c2p3 and c2p7 assigned to ___
problems c2p1 and c2p2 assigned to ____
problems c2p5 and c2p6 assigned to ___
5 1/26 The Forecast Process, Data Considerations, and Model Selection --Chapter 2 (continued) --
"Inference and Mean and t-statistics" video
Examine the following two data sets using the techniques in these two videos:
6 1/31 The Forecast Process, Data Considerations, and Model Selection --Chapter 2 (continued) --
(Fish Prices (correlation) assigned to __
Age and Height (correlation) assigned to __
The 1970 Draft Lottery (correlation) assigned to __
Alcohol and Tobacco (correlation) assigned to ___
When Do Babies Start To Crawl? (correlation) assigned to ____
Brainsize and Intelligence (correlation) assigned to ____
Smoking and Cancer (correlation) assigned to ____
7 2/2 The Forecast Process, Data Considerations, and Model Selection -- Chapter 2 (continued)
Home Run Story assigned to __________________
Calcium and Blood Pressure (t-test) assigned to __________________
Friday The Thirteenth (t-test) assigned to __________________
Random Dot Stereograms (t-test) assigned to __________________
Helium Football (t-test) assigned to __________________
Singer Heights (t-test) assigned to __________________
Resins Rid Termites (t-test) assigned to __________________
8 2/7 Moving Averages and Exponential Smoothing -- Chapter 3
9 2/9 Moving Averages and Exponential Smoothing -- Chapter 3 and Event Studies (continued)
10 2/14 Moving Averages and Exponential Smoothing -- Chapter 3 and Event Studies (continued)
Growth Model - Gompertz's Law assigned to _____________________
Condiment I Problem (do not include "events" in the analysis) assigned to ___
Condiment II Problem (include "events" in the analysis) assigned to __
Disinfectant I Problem (do not include "events" in the analysis) assigned to ____
Disinfectant II Problem (include "events" in the analysis) assigned to ____
The 1970 Draft Lottery (smoothing) assigned to __
12 2/21 Introduction to Forecasting with Regression Methods --Chapter 4
13 2/23 Introduction to Forecasting with Regression Methods --Chapter 4
Chapter 4, Case #1 and Data for Case #1 (Estimating Intel's Financial Beta)
Chapter 4, Case #2 and Data for Case #2 (Autocorrelation and Spurious Regression)
14 2/28 Chapter 4 (continued) --
problems c4p4 and c4p5 assigned to ___
problems c4p6 , c4p7 assigned to __
problem c4p8 assigned to __
problem c4p9 assigned to __
problem c4p10 assigned to ________
problem c4p11 assigned to _________
15 3/2 -- Introduction to Forecasting with Regression Methods --Chapter 4 (continued)
problem c4p12 assigned to ________
problem c4p13 assigned to ________
Create a causal simple regression I with original data. assigned to _______
Create a causal simple regression II with original data. assigned to _______
Create a causal simple regression III with original data. assigned to ________
Olympic Swimming Regressions (from the Olympic Swimming Database at Princeton University)
Midsemester Break March 5 - March 13
16 3/14 Introduction to Forecasting with Regression Methods --Chapter 4 (continued)
17 3/16 Forecasting with Multiple Regression -- Chapter 5
18 3/21 Forecasting with Multiple Regression -- Chapter 5 (continued)
problem c5p5 assigned to _________
problems c5p6 and c5p7 assigned to _______
problem c5p8 assigned to _______ (skip part "d")
problem c5p9 assigned to _______
problem c5p10 assigned to ______ (use the "Economagic" site to collect data)
Chapter 5, Case #1 and Data for Case #1 (Near Multicollinearity)
Chapter 5, Case #2 and Data for Case #2 (Dealing With Seasonal Data)
19 3/23 Forecasting with Multiple Regression -- Chapter 5 (continued)
Agricultural Economics (regression) assigned to ________
Air Pollution (regression) assigned to ________
Beef Council Checkoff (regression) assigned to _______
Egyptian Skull Development (regression) assigned to ______
Enrollment Forecast (regression) assigned to ________
Food Taste Test (regression) assigned to _______
Forecasting Appliance Sales (regression) assigned to ________
US Crime Story (lurking variable) assigned to ____________________
Easter Break: Friday,March 25 - Monday, March 28
(no classes Friday or Monday)
20 3/30 Forecasting with Multiple Regression -- Chapter 5 (continued)
Fuel Efficient Buick (lurking variable) assigned to _______________________
Forecasting Retail Sales (regression) assigned to ______
Healthy Breakfast (regression) assigned to ________
Ice Cream Consumption (regression) assigned to ________
Massachussetts Lunatics (regression) assigned to _______
Mercury Contamination (regression) assigned to _______
Mexico City Effect (regression) assigned to _______
Nuclear Power (regression) assigned to _________
Parking Meter Theft (dummy variable) assigned to _____________________
Police Manpower (dummy variable) assigned to ___________________
Factors in Country Inflation (dummy variable) assigned to ________________
Teacher Pay By State (dummy variable) assigned to ________________________
21 4/4 Forecasting with Multiple Regression -- Chapter 5 (continued)
Cheddar Cheese (Near Multicollinearity) assigned to _______________________
OECD Economic Development (regression) assigned to _________
Polishing Times (regression) assigned to _______
Quarterback and Team Salaries (regression) assigned to ________
Taxes and Home Prices (regression) assigned to ________
Transformation (regression) assigned to ________
TV Add Yields (regression) assigned to ________
22 4/6 Second Midterm Examination (Lecture Slides Here)
23 4/11 Time-Series Decomposition --Chapter 6
24 4/13 Time-Series Decomposition --Chapter 6 (continued)
problem c6p7 assigned to ______
problem c6p8 assigned to _______
problem c6p11 assigned to _______
problem c6p6 assigned to _____
problem c6p9 assigned to _______
problem c6p12 assigned to _______
Create a time-series decomposition I with original data. assigned to ______
Create a time-series decomposition II with original data. assigned to ________
Chapter 6 Case 1 (Budget Deficit Forecast) Data here
Chapter 6 Case 2 (Car Sales) Data here
25 4/18 Box-Jenkins (ARIMA) Type Forecasting Models -- Chapter 7
26 4/20 Box-Jenkins (ARIMA) Type Forecasting Models -- Chapter 7 (continued)
problem c7p5, data series "a" and "b" assigned to _________
problem c7p5, data series "c" and "d" assigned to ________
problem c7p6, data series "a" and "b" assigned to _________
problem c7p6, data series "c" and "d" assigned to ________
Chapter 7 Case 2 (Foreign Car Sales) Data here
27 4/25 Combining Forecast Results - Chapter 8
Term Projects Due (Graduate Students Only)
problem c8p3 assigned to _______
problem c8p4 assigned to ______
problem c8p5 assigned to _______
28 4/27 Data and Stories
Graduation Rates (time series) assigned to ________
Emerald Health Network (time series and regression) assigned to _______
Highway Fatalities (time series) assigned to _______
Oil Production (time series) assigned to _______
Olympic Trends (time series) assigned to ______
Acorn Size and Oaks assigned to _______
USA Auto Mileage (regression) assigned to ________
USA Temperatures (regression) assigned to ________
Wages and Hours (regression) assigned to _______
Reigning In The Wild Horses (regression) assigned to ________