Lecture notes on forecasting

   Robert Nau, Fuqua School of Business, Duke University


RegressIt: free Excel add-in for linear regression and multivariate data analysis


1.  Get to know your data

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


2.   Introduction to forecasting

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


3.   Averaging and smoothing models

Averaging and exponential smoothing models
Spreadsheet implementation of seasonal adjustment and exponential smoothing


4.  Regression to mediocrity

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
Testing the assumptions of linear regression
Stepwise and all-possible-regressions


5.  Introduction to ARIMA models

Introduction to ARIMA: nonseasonal models
Identifying the order of differencing
Identifying the orders of AR or MA terms
Estimation of ARIMA models

Seasonal differencing
Seasonal random walk
Seasonal random trend

Seasonal ARIMA models
Summary of rules for identifying ARIMA models
ARIMA models with regressors


6. Choosing the right forecasting model

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
How to avoid trouble


 


Last updated May 19, 2014