ECON 694: Business Forecasting
Prerequisite: Admission to the MBA program or graduate status.
Credit Hours: (3)
Forecasting involves making the best possible judgment about some future event. Topics covered include introduction to forecasting, a review of basic statistical concepts, exploring data patterns and choosing a forecasting technique, moving averages and smoothing models, regression analysis, time series analysis, the Box-Jenkins (ARIMA) methodology and judgmental elements in forecasting. Students will be trained in using computer-based models, databases and programs.
Detailed Description of Course
1) An introduction to forecasting
2) Review of statistics and microsoft excel
3) Visual forecasting (Why it's important to make good graphs.
4) Decomposing data: trends, seasons and cycles
5) Trend forecasting methods
a. Regression
b. Smoothing
6) Seasonal Forecasting Methods
7) Cycle Forecasting Methods
8) Multiple regression for time series
9) Ensemble forecasts
10) Classification: regression trees, logit models, neural networks
11) Judgement-based forecasts: experts, surveys, the delphi method, prediction markets
12) Putting forecasting to work
a. The forecasting process
b. Making forecasts valuable to the end-user
c. Forecast evaluation and monitoring
Detailed Description of Conduct of Course
Lectures and class discussion will be the primary teaching methods. The course will combine the use of lectures, videotapes, guest speakers, reading materials, project reports, and case analysis. Students will receive hands-on computer experience. In addition, forecasting techniques based on subjective and judgmental methods and their applications in long-range forecasts will be discussed at length.
Goals and Objectives of the Course
After successfully completing this course, students will be able to:
1) Describe the role of forecasting within organizations;
2) Construct and assess trend and seasonal forecasting models;
3) Construct and assess mixed moving average and auto-regressive (cycle) forecasting
models (ARIMA):
4) Justify model selection and compare different models using measures of fit and
common sense;
5) Perform predictive classification using regression trees, logistic regression and
neural networks;
6) Describe judgmental forecasting methods and their applications;
7) Describe the uses and limitations of different forecasting techniques;
8) Demonstrate proficiency using software such as SAS/JMP and Excel to create and
evaluate forecasts.
9) Analyze how changes in economic variables affect decisions of firms, households
and equilibrium in markets (SLO6).
10) Analyze how changes in macroeconomic variable (e.g., consumption, business investment,
government spending, or foreign trade) affect the national economy (SLO5).
Assessment Measures
Student progress will be evaluated through assignments, exams, projects and presentations.
Other Course Information
The instructor will be expected to integrate substantial readings from the current and/or original professional literature into the course.
Approval and Revision Dates
May 1, 2018
4/17/00 New course approved