Decision 411 Course Outline

- 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

Preassignment: install
Statgraphics and read tutorial
handout

Videos: #0, 1, 2, 3

Lecture materials : Slides Handouts Homework assignment #1

Videos: #0, 1, 2, 3

Lecture materials : Slides Handouts Homework assignment #1

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

- 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

- 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

- 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

- 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

- 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:

*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:

- 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

- 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:

Introduction to ARIMA:
nonseasonal models

Identifying the order of differencing

Identifying the orders of AR or MA terms

Estimation of ARIMA models

Identifying the order of differencing

Identifying the orders of AR or MA terms

Estimation of ARIMA 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

- 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:

ARIMA models with
regressors

Steps in choosing a forecasting model

Forecasting flow chart

Data transformations and forecasting models: what to use and when

Steps in choosing a forecasting model

Forecasting flow chart

Data transformations and forecasting models: what to use and when

- 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

*Last updated October 2
2007. Always under construction.*