Econ. 674  Business Forecasting			
Inst. N. Hashemzadeh

May 2001							Office: Davis 111
Tel: 831-5888							e-mail nahsehmz@runet.edu

Class hours (MWF) 5-10 p.m. (tentative)				Class:Whitt 222
Text: Business Forecating: Holt Wilson and Barry Keating  

Software: will be provided by the publisher and comes with the book.

N.B. This is a tentative course outline and may be modified based on needs, academic preparation and other considerations.

Part 1. Get to know your data and the software
a.	Overview of forecasting: how do you predict the future? What kind of accuracy is possible?
b.	Where to obtain data; data sources 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 Minitab

Additional 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

Part 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

Mean

Linear trend

Random walk

Random walk with growth

Geometric random walk

Three types of forecasts: estimation period, validation period, and the
future

HOMEWORK ASSIGNMENT #1

Part 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

Addtional notes:

Seasonal differencing

Seasonal random walk

Seasonal random trend

Data set for assignment #2

Part 4. Averaging and smoothing models

a.	Simple moving average model

b.	Exponential smoothing model

c.	Combination of smoothing and seasonal adjustment

d.	Robust models for noisy data

e.	Massively parallel forecasting

Reading: Remainder of Chapter 5 (moving averages and smoothing methods)
Additional notes:

Averaging and exponential smoothing models

Spreadsheet implementation of seasonal adjustment and exponential smoothing 

Part 5. Time series regression models

a.	Fitting time series regression models

b.	Fitting time series regression models

c.	What to look for in regression output

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HW#3 due

d.	What's a good value for R-squared?

e.	Not-so-simple regression models

DATA SET FOR ASSIGNMENT #4

Part 6. 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

Reading: Chapter 10

• 
  Introduction to ARIMA: nonseasonal models
  Identifying the order of differencing
  Identifying the orders of AR or MA terms
  Rules for identifying ARIMA models

• 
  Part 7. ARIMA continued; seasonal models
  HW#4 due
  a.	Identification of seasonal models
  b.	Examples of seasonal model-fitting
  c.	Spreadsheet implementation

• Additional notes:
  Estimation of ARIMA models
  Seasonal ARIMA models

• 
  Part 8. ARIMA wrapup, automation
  Designated project" option (there is no HW#5)
  a.	Combination of ARIMA and regression models
  b.	Recap of steps in choosing a forecasting model
  c.	Automatic forecasting software

• Additional notes:
  ARIMA models with repressors
  Steps in choosing a forecasting model
  Forecasting flow chart
  Automatic forecasting software

• 
  Part 9. Politics and ethics
  a.	Political and ethical issues in forecasting
  b.	Review of models: what to use and when

• Reading: Chapter 11
  Political and ethical issues in forecasting
  How to avoid trouble
  Data transformations and forecasting models: what to use and when 

A detailed schedule of assignments will be posted on the web page later.