TM 745 Forecasting for Business

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TM 745 Forecasting for Business TechnologyDr.
Frank Joseph Matejcik
4th Session 2/25/08 Chapter 4 Introduction to
Forecasting with Regression Methods

• South Dakota School of Mines and Technology,
  Rapid City

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Agenda New Assignment

• ch4(6,10) Tentative Schedule
• Chapter 4 WK (with odd diversions)
• Try to use ForecastX for Autocorrelation
• Business Forecasting 5th Edition J. Holton
  Wilson Barry KeatingMcGraw-Hill

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Tentative Schedule
Chapters Assigned 28-Jan 1 problems
1,4,8 e-mail, contact 4-Feb 2 problems 4,
8, 9 11-Feb 3 problems 1,5,8,11 18-Feb
Presidents Day 25-Feb 4 problems 6,10 3-Mar
5 problems 5,8 10-Mar Exam 1 Ch 1-4
Revised 17-Mar Break 24-Mar Easter 31-Mar
6 problems 4, 7
Chapters Assigned 7-Apr 7 3,4,5(series
A) 7B 21-Apr 8 Problem 6 28-Apr
9 05-May Final
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Web Resources

• Class Web site on the HPCnet system
• http//sdmines.sdsmt.edu/sdsmt/directory/courses/2
  008sp/tm745M021
• Streaming video http//its.sdsmt.edu/Distance/
• Answers will be online. Linked from
• The same class session that is on the DVD is on
  the stream in lower quality. http//www.flashget.c
  om/ will allow you to capture the stream more
  readily and review the lecture, anywhere you can
  get your computer to run.

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Introduction to Forecasting with Regression
Methods

• Fundamentals
• Jewelry
• Disposable Income
• Gap

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The Bivariate Regression Model
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The Bivariate Regression Model
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Visualizationof DataImportant in
Regression
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A Process for Regression Forecasting

• Inspect data (graphically) trends, seasonals,
  cycles, and outliers
• Make forecasts for all the Xs (predictors,
  independent variables)
• estimate coefficients (use a holdout)
• compare various models

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Forecasting with a Simple Linear Trend
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Forecasting with a Simple Linear Trend
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Forecasting with a Simple Linear Trend
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Forecasting with a Simple Linear Trend
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Forecasting with a Simple Linear Trend
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Using a Causal Regression Model to Forecast

• Not using a trend line
• Yf(X) where X is an appropriate explanatory
  variable
• Use knowledgeable people library for Xs
• Logical construct (Jevons sunspottheory of
  business cycles)
• Try to forecast Jewelry sales

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A Jewelry Sales Based on Disposable Personal
Income
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A Jewelry Sales Based on Disposable Personal
Income
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A Jewelry Sales Based on Disposable Personal
Income
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Jewelry Sales Based on fig 4.7 Disposable
Personal Income
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Jewelry Sales Based on fig 4.7 Disposable
Personal Income
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Jewelry Sales Based on fig 4.7 Disposable
Personal Income
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Statistical Evaluation of Regression Models tab
4.5
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Statistical Evaluation of Regression Models tab
4.5
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Statistical Evaluation of Regression Models tab
4.5
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Statistical Evaluation of Regression Models

• 1. Check to see if the sign of the slope makes
  sense
• 2. Check the significance of the slope using a
  t-test.
• 3. How much of the variation is explained by the
  regression using R2

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Using the Standard Error of the Estimate
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Serial Correlation
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Serial Correlation
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Serial Correlation Fixes

• 1. First differencing the data
• 2. Use multiple regression extra variables
• 3. Use the square of the existing causal variable
  as another variable
• 4. Advanced models includingserial correlation.

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Heteroscedasticity
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Heteroscedasticity
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Heteroscedasticity Fixes

• Transformation
• logarithm
• square root
• others
• Non least squares regression

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Cross-Sectional Forecasting

• One time period
• Another explanatory variable
• Similar to causal methods, but data is separated.
• The population of cities wasthe predictor
  variable.

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Forecasting Total Houses Sold Sales w/ Bivariate
Regression
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Forecasting Total Houses Sold Sales w/ Bivariate
Regression
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Integrative Case The Gap
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Solutions toCase Questions 1
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Solutions toCase Questions 2
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Solutions toCase Questions 2
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Solutions toCase Questions 2
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Solutions toCase Questions 2
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Using ForecastX to Make Regression Forecasts

• Try it?
• Try it?
• Next week more on regression
• Maybe, that weird plot will beexplained

Further Comments on Regression Models