Economics 4312
Business Cycles and Forecasting
Dr. Tom Kelly
Office: A.301.2 Hankamer
Phone: 710-4146
Fax: 710-6142
E-Mail: Tom_Kelly@baylor.edu
Home
Page: http://hsb.baylor.edu/html/kellyt/home.htm
Purpose of
the Course: To introduce you to the concepts, tools, and
techniques used by economists for business forecasting.
The
Textbook: Hanke and Wichern, Business
Forecasting, (8thd edition)
General
Requirements:
All students will be asked to read and work
computer lab assignments based upon the textbook, participate in class
discussions of course content, and perform written examinations based upon
course content. Assignments are due no
more than two days after they are assigned. To prepare for each class session, refer to
the schedule below.
Additional
Graduate Student Requirement: In order to receive graduate credit each
graduate student in addition to general requirements must also develop a
statistical forecast of the future performance of an assigned industry. Focus will be placed on demand estimation of
industry performance using multiple regression analysis using panel data from
published (or on line) data sources.
Students are expected to write a report to describe their forecasting
methods and findings and how their results may be used with respect to current
and future business strategy of a firm in that industry.
Computer
Requirements: We will
use Excel as our primary statistical package, using the MegaStat feature in
addition to normal spread sheet calculations.
Grades
will be based upon the following requirements:
·
Undergraduate
students
o
Two one-hour in class exams worth 20% each and a
comprehensive final exam worth 30% of your final grade.
o
Five written lab assignments and class discussion
worth 30% for undergraduate students.
·
Graduate
students
o
Two hourly exams valued at 20% each and comprehensive
final exam valued at 30%.
o
Completion of all lab assignments and class
participation in order to receive credit in the course.
o
Completion of additional research and report on
estimated demand forecast for selected industry valued at 30%.
Course Topics
Chapter 1: Introduction to Forecasting
The history of forecasting
The need for forecasting
Types of forecasts
Macroeconomic v.
microeconomic forecasting
Choosing a forecasting
method
Steps in forecasting
Managing the forecasting
process
Computer forecasting
packages
Online information
Chapter 2: A Review of Basic Statistical Concepts
Descriptive statistics
Probability distributions
Sampling distributions
Inference from a sample
Estimation and
hypothesis testing
Chapter 3: Exploring Data Patterns and Choosing a
Forecasting Technique
Exploring time series data patterns
Using autocorrelation
analysis
Choosing a technique
based on autocorrelation analysis
Measuring forecasting
error
Determining the adequacy
of a forecasting technique
Lab Assignment 1: Using Eviews to identify data
patterns
Chapter 4: Moving Averages and Smoothing Methods
Naïve models
Simple and moving
averaging models
Double moving average
models
Single exponential smoothing models
Double exponential smoothing models
Holt model to adjust for trend
Holt-Winters model to adjust for trend and seasonal
variation
Application to management
Lab Assignment 2:
Using MegaStat to forecast with time series models
Chapter 5: Time Series and Their Components
Decomposition model
Trend curves and
forecasts
Seasonal adjustment and
forecasting a seasonal time series
Cyclical
and irregular adjustment
The Census II
decomposition method
Application to
management
Lab Assignment 3: Using
MegaStat to forecast with a decomposition model.
Exam I on Chapters 1 - 5
Chapter 6: Simple Linear Regression
Least squares regression
line specification and estimation
Evaluation of regression
results
Point and interval
forecasts of the dependent variable
Coefficient of
determination
Analysis of residuals
Variable transformations
Growth curves
Application to management
Chapter 7: Multiple Regression Analysis
Selection of predictor variables
Evaluation of
correlation matrix
Estimation of a multiple
regression model
Interpreting regression
coefficients
Point and interval
forecasts
Dummy variables
Multicollinearity
Diagnostics and residual
analysis
Application to
management
Lab Assignment 4: Using
MegaStat to forecast with a multiple regression model.
Chapter 8: Regression with Time Series Data
The problem of autocorrelation
Durbin-Watson test for
autocorrelation of error terms
Solutions to
autocorrelation problems
Model specification
error
Generalized least
squares
Autoregressive models
The problem of
heteroscedasticity
Application to
management
Lab Assignment 5: Using
Excel MegaStat to eliminate autocorrelation.
Exam II on Chapters 6 - 8
Chapter 9: The Box-Jenkins (ARIMA) Methodology
Determining a model using the
Box-Jenkins methodology
Implementing the
model-building strategy
Application to
management
Chapter 10: Judgmental Forecasting and Forecast
Adjustments
The Dephi Method
Scenario writing
Combining forecasts
Summary of judgmental
forecasting
Chapter 11: Managing the Forecasting Process
The forecasting process
Monitoring forecasts
Forecasting
responsibility
Forecasting costs
Forecasting and the MIS
system
Selling forecasting to
management
The future of
forecasting
Final Exam on Chapters 1 - 11