
Decision 411 Course Outline
WEEK 1
Wednesday,
September 5: Get to know your data
- Overview of forecasting: how do you predict the future?
What kind of
accuracy is possible?
- Where to obtain data; data sources at Fuqua and on the web
- How to move data around: useful things you can do with
your word
processor and spreadsheet
- What to look for in data: seasonality, inflation, trends,
cycles,
etc.
- How to transform data to reveal its structure; deflation,
logging,
seasonal adjustment
- Illustration of basic operations in Statgraphics
- Forecasts and confidence intervals for the simplest case:
the "mean" model
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
Mean (constant) model
Friday,
September 7: Introduction to forecasting
- Forecasting a nonstationary series I: the trend line model
- Forecasting a nonstationary series II: the random walk
model
- How to identify a random walk: differencing and
autocorrelation
analysis
- Geometric random walk: the basic stock price model
- Three types of forecasts: estimation period, validation
period,
and extrapolations into the future
- How to evaluate forecast errors and compare models
Videos: #7, 8, 9, 10, 11, 12
Lecture materials :
Slides Handouts Data
files
Lecture notes:
Linear trend model
Random walk model
Random walk
model with drift
Geometric random
walk
model
Three types of
forecasts:
estimation period, validation period, and the future
WEEK 2
Tuesday,
September 11: Homework assignment #1 due by 8am
Modeling
of seasonality
- General considerations in working with seasonal data:
causes of
seasonality, stability of seasonal patterns
- Seasonal random walk; and seasonal random trend models
- Seasonal adjustment by the ratio-to-moving-average method
- Additive versus multiplicative seasonal adjustment
- Adjustments for holidays and trading days
- Trend/cycle decomposition of time series
Videos: #13, 14
Lecture materials :
Slides Handouts Homework assignment
#2
Friday,
September 14: Averaging and smoothing models
- Simple moving average model
- Exponential smoothing models
- Combination of smoothing and seasonal adjustment
Lecture materials :
Slides Handouts
SES spreadsheet
LES+seasonal adjustment
spreadsheet
Lecture notes:
Averaging and
exponential
smoothing models
Spreadsheet
implementation
of seasonal adjustment and exponential smoothing
WEEK 3
Tuesday,
September 18: Homework assignment #2 due by 8am
Regression
models
- Indiana Jones and the temple of R-squared
- Correlation coefficients
- Fitting simple regression models; interpreting output
- Using lagged and differenced variables in regression
models
- Confidence intervals for regression forecasts
Videos: #4, 5, 6
Lecture materials :
Slides Handouts Regression formula spreadsheet
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
Friday,
September 21: Time series regression models
- Economic interpretation of regression coefficients
- Using auto- and cross-correlation plots to identify useful
lags of variables
- Dummy variables
- How to model seasonality with regression
- Log-log (constant elasticity) models
Videos: #15, 16
Lecture materials :
Slides Handouts
Lecture notes:
WEEK 4
Tuesday,
September 25: Regression continued
- IN-CLASS QUIZ
- Confidence limits for sums of coefficients
- Use of the time index as a regressor
- Predicting the future with regression models
Lecture materials :
Slides Handouts Homework assignment
#3
HW#3 data file
Lecture notes:
Friday,
September 28: Advanced regression methods, GLM and ANOVA
- Stepwise and all-subsets regression
- Categorical independent variables (ANOVA)
- General linear models (GLM)
- Regression models with hold-out samples
Videos: #17, 18
Lecture materials :
Slides Handouts Data
files
Lecture notes:
Testing the assumptions
of linear regression
Stepwise and
all-possible-regressions
WEEK 5
Tuesday,
October 2: Homework assignment #3 due by 8am
Introduction
to ARIMA models
- Random walk + Autoregressive + Exponential Smoothing =
ARIMA
- Using ACF and PACF plots to determine the "signature" of a
time
series
- Fitting non-seasonal ARIMA models
- The spectrum of ARIMA models
Videos: #19
Lecture materials :
Slides Handouts Data
files
Lecture notes:
Friday,
October 5: ARIMA continued; seasonal models
- Identification of seasonal models
- Examples of seasonal model-fitting
- Spreadsheet implementation
Videos: #20, 21, 22
Lecture materials :
Slides Handouts Data
files
Lecture notes:
Seasonal
differencing
Seasonal
random walk
Seasonal
random trend
Seasonal ARIMA models
Summary of rules for
identifying ARIMA models
WEEK 6
Tuesday,
October 9: ARIMA with regressors, sales of new products
- Combination of ARIMA and regression models
- Sales of new products: the BASS model
- Recap of steps in choosing a forecasting model
- Review of models: what to use and when
Lecture materials :
Slides Handouts
Data
files
Lecture notes:
Friday,
October 12: Automatic forecasting; Political,
ethical, and management issues
- Automatic forecasting software
- Political and ethical issues in forecasting
Lecture materials :
Slides Handouts Data
files
Lecture notes:
Automatic forecasting
software
Political and ethical
issues in forecasting
How to avoid trouble
FINALS WEEK
Wednesday,
October 17: Final project due at 12 noon
Last updated October 2
2007. Always under construction.