Baylor University


Economics 4312

Business Cycles and Forecasting


Dr. Tom Kelly

Office: A.301.2 Hankamer

Phone: 710-4146

Fax: 710-6142


Home Page:

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

Nave 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


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