Decision 411 Course Outline

WEEK 1

Wednesday, September 5:  Get to know your data

1. Overview of forecasting: how do you predict the future? What kind of accuracy is possible?
2. Where to obtain data; data sources at Fuqua and on the web
3. How to move data around: useful things you can do with your word processor and spreadsheet
4. What to look for in data: seasonality, inflation, trends, cycles, etc.
5. How to transform data to reveal its structure; deflation, logging, seasonal adjustment
6. Illustration of basic operations in Statgraphics
7. 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

1. Forecasting a nonstationary series I: the trend line model
2. Forecasting a nonstationary series II: the random walk model
3. How to identify a random walk: differencing and autocorrelation analysis
4. Geometric random walk: the basic stock price model
5. Three types of forecasts: estimation period, validation period, and extrapolations into the future
6. 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

Link for submitting homework assignment

Modeling of seasonality

1. General considerations in working with seasonal data: causes of seasonality, stability of seasonal patterns
2. Seasonal random walk; and seasonal random trend models
3. Seasonal adjustment by the ratio-to-moving-average method
4. Additive versus multiplicative seasonal adjustment
5. Adjustments for holidays and trading days
6. Trend/cycle decomposition of time series

Videos: #13, 14

Lecture materials :  Slides    Handouts    Homework assignment #2 

Friday, September 14:   Averaging and smoothing models

1. Simple moving average model
2. Exponential smoothing models
3. 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

Link for submitting homework assignment

Regression models

1. Indiana Jones and the temple of R-squared
2. Correlation coefficients
3. Fitting simple regression models; interpreting output
4. Using lagged and differenced variables in regression models
5. 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

1. Economic interpretation of regression coefficients
   
2. Using auto- and cross-correlation plots to identify useful lags of variables
3. Dummy variables
   
4. How to model seasonality with regression
5. Log-log (constant elasticity) models
   

Videos: #15, 16

Lecture materials :  Slides    Handouts   

Lecture notes:

WEEK 4

Tuesday, September 25:  Regression continued

1. IN-CLASS QUIZ
2. Confidence limits for sums of coefficients
3. Use of the time index as a regressor
   
4. 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

1. Stepwise and all-subsets regression
2. Categorical independent variables (ANOVA)
3. General linear models (GLM)
4. 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

Link for submitting assignment

Introduction to ARIMA models

1. Random walk + Autoregressive + Exponential Smoothing = ARIMA
2. Using ACF and PACF plots to determine the "signature" of a time series
3. Fitting non-seasonal ARIMA models
4. The spectrum of ARIMA models

Videos: #19

Lecture materials :  Slides    Handouts    Data files

Lecture notes:

Friday, October 5: ARIMA continued; seasonal models

1. Identification of seasonal models
2. Examples of seasonal model-fitting
3. 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

1. Combination of ARIMA and regression models
2. Sales of new products:  the BASS model
   
3. Recap of steps in choosing a forecasting model
4. 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

1. Automatic forecasting software
2. 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

"Designated" project options

Guidelines for final project

Examples of custom projects from past years

Link for submitting final project

Last updated October 2 2007.  Always under construction.