Course Outline
Lecture 1. Get to know your data
a. Overview of forecasting: how do you predict the future? What kind of
accuracy is possible?
b. Where to obtain data; data sources at Fuqua and on the web
c. How to move data around: useful things you can do with your word
processor and spreadsheet
d. What to look for in data: seasonality, inflation, trends, cycles,
etc.
e. How to transform data to reveal its structure; deflation, logging,
seasonal adjustment
f. Illustration of basic operations in Statgraphics
Reading preassignment: Statgraphics Tutorial Introduction
Lecture notes:
Famous forecasting quotes
How to shovel data around
Get to know your data
Inflation adjustment (deflation)
Seasonal adjustment
Stationarity and differencing
The logarithm transformation
Lecture 2. Introduction to forecasting
a. Forecasting a stationary series: the "mean" model
b. Forecasting a nonstationary series I: the trend line model
c. Forecasting a nonstationary series II: the random walk ("naive")
model
d. How to identify a random walk: differencing and autocorrelation
analysis
e. Geometric random walk: the basic stock price model
f. Three types of forecasts: estimation period, validation period,
and long-term extrapolation
g. How to evaluate forecast errors and compare models
Lecture notes:
Mean (constant) model
Linear trend model
Random walk model
Random walk model with growth
Geometric random walk model
Three types of forecasts: estimation period, validation
period, and the future
Lecture 3. Modeling of seasonality
HW#1 due
a. General considerations in working with seasonal data: causes of
seasonality, stability of seasonal patterns
b. Seasonal random walk; and seasonal random trend models
c. Seasonal adjustment by the ratio-to-moving-average method
d. Additive versus multiplicative seasonal adjustment
e. Adjustments for holidays and trading days
f. Trend/cycle decomposition of time series
Lecture notes:
Seasonal differencing
Seasonal random walk
Seasonal random trend
Working with seasonal data
Lecture 4. Averaging and smoothing models
a. Simple moving average model
b. Exponential smoothing model
c. Combination of smoothing and seasonal adjustment
Lecture notes:
Averaging and exponential smoothing models
Spreadsheet implementation of seasonal adjustment
and exponential smoothing
Lecture 5. Regression to mediocrity
HW#2 due
a. Indiana Jones and the temple of R-squared
b. Correlation coefficients
c. Fitting simple regression models; interpreting output
d. Confidence intervals for regression forecasts
Lecture notes:
Introduction to regression analysis
What to look for in regression output
What's the bottom line? How to compare models
Additional notes on regression analysis
Spreadsheet for illustrating regression formulas
Lecture 6. Time series regression models
a. Using lagged and differenced variables in regression models
b. Interpreting auto- and cross-correlation plots
Lecture notes:
Fitting time series regression models
Example of regression analysis: predicting auto
sales from personal income
Lecture 7. Regression continued
HW#3 due
a. What's a good value for R-squared?
b. Not-so-simple regression models
Lecture notes:
What's a good value for R-squared?
Not-so-simple regression models
Lecture 8. Regression wrapup
a. The four horsemen of linear regression
b. Stepwise and all-subsets regression
c. Nonlinear regression
d. Business cycle indicators
Lecture notes:
Testing the assumptions of linear regression
Stepwise and all-possible-regressions
Nonlinear regression
Business Cycle Indicators
Lecture 9. Introduction to ARIMA models
a. Naive + Autoregressive + Exponential Smoothing = ARIMA
b. Using ACF and PACF plots to determine the "signature" of a time
series
c. Fitting non-seasonal ARIMA models
d. The spectrum of ARIMA models
Lecture notes:
Introduction to ARIMA: nonseasonal models
Identifying the order of differencing
Identifying the orders of AR or MA terms
Estimation of ARIMA models
Lecture 10. ARIMA continued; seasonal models
HW#4 due
a. Identification of seasonal models
b. Examples of seasonal model-fitting
c. Spreadsheet implementation
Lecture notes:
Seasonal ARIMA models
Summary of rules for identifying ARIMA models
GUIDELINES FOR FINAL PROJECTS
Lecture 11. ARIMA wrapup, automation
a. Combination of ARIMA and regression models
b. Recap of steps in choosing a forecasting model
c. Automatic forecasting software
Lecture notes:
ARIMA models with regressors
Steps in choosing a forecasting model
Forecasting flow chart
Data transformations and forecasting models:
what to use and when
Lecture 12. Politics and ethics
a. Political and ethical issues in forecasting
b. Review of models: what to use and when
Lecture notes:
Automatic forecasting software
Political and ethical issues in forecasting
How to avoid trouble
Final projects are due at 5pm on the last day of exam week
Last updated February 28, 2000. Always under construction.
