Regression and
Forecasting Models
Professor William Greene
Stern School of Business
IOMS Department
Department of Economics
0-1/17
Part 0: Introduction
Regression and
Forecasting Models
Part 0 - Introduction
0-2/17
Part 0: Introduction
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Professor William Greene;
Economics and IOMS Departments
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Office: KMEC, 7-90 (Economics Department)
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Office phone: 212-998-0876
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Email: wgreene@stern.nyu.edu
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URL: http://people.stern.nyu.edu/wgreene
http://people.stern.nyu.edu/wgreene/regression/Outline.htm
0-3/17
Part 0: Introduction
Course Objectives
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Basic understanding: The regression model
as a framework for the analysis of
relationships among variables
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Technical know how: How to formulate a
regression model, estimate its parameters,
and understand the implications of the
estimated model.
0-4/17
Part 0: Introduction
We used
McDonald’s
Per Capita
0-5/17
Part 0: Introduction
Macs and Movies
Countries and Some of the Data
Code
Pop(mm) per cap
Income
1 Argentina
37
12090
2 Chile,
15
9110
3 Spain
39
19180
4 Mexico
98
8810
5 Germany
82
25010
6 Austria
8
26310
7 Australia
19
25370
8 UK
60
23550
0-6/17
# of
McDonalds
173
70
300
270
1152
159
680
1152
Language
Spanish
Spanish
Spanish
Spanish
German
German
English
UK
Genres (MPAA)
1=Drama
2=Romance
3=Comedy
4=Action
5=Fantasy
6=Adventure
7=Family
8=Animated
9=Thriller
10=Mystery
11=Science Fiction
12=Horror
13=Crime
Part 0: Introduction
Movie Genres
0-7/17
Part 0: Introduction
Movie Madness Data (n=2198)
0-8/17
Part 0: Introduction
0-9/17
Part 0: Introduction
Case Study Using A Regression
Model: A Huge Sports Contract
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0-10/17
Alex Rodriguez hired by the Texas Rangers for
something like $25 million per year in 2000.
Costs – the salary plus and minus some fine
tuning of the numbers
Benefits – more fans in the stands.
How to determine if the benefits exceed the
costs? Use a regression model.
Part 0: Introduction
Baseball Data
(Panel Data – 31 Teams, 17 Years)
0-11/17
Part 0: Introduction
A Regression Model
Attendance(team,this year) = α team
+ γ Attendance(team, last year)
+ β1Wins (team,this year)
+ β 2 Wins(team, last year)
+ 3 All_Stars(team, this year)
+ (team, this year)

0-12/17
Part 0: Introduction
 = .54914
1 = 11093.7
2 = 2201.2
3 = 14593.5
Effect of 1 more win
11093.7  2201.2
 32757
1  .59414
Effect of adding an All Star
14593.5
=
 35957
1  .59414
=
0-13/17
Part 0: Introduction
Marginal Value of an A Rod
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0-14/17
8 games * 32,757 fans + 1 All Star = 35957
= 298,016 new fans
298,016 new fans *
 $18 per ticket
 $2.50 parking etc.
 $1.80 stuff (hats, bobble head dolls,…)
$6.67 Million per year !!!!!
It’s not close.
(Marginal cost is at least $16.5M / year)
Part 0: Introduction
Course Prerequisites
Basic algebra. (Especially summation)
 Geometry (straight lines)
 Logs and exponents
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NOTE: I (you) will use only base e (natural)
logs, not base 10 (common) logs in this
course.
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Previous course in basic statistics – up to
testing a hypothesis about a mean
0-15/17
Part 0: Introduction
Course Materials
http://people.stern.nyu.edu/wgreene/regression/Outline.htm
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0-16/17
Notes: Distributed in first class
Text: McClave, Benson, Sincich; Statistics for
Business and Economics (2nd Custom NYU
edition), Pearson, 2011.
On the course website:
 Class slide presentations
 Problem sets
 Data sets for exercises
Part 0: Introduction
Course Software: Minitab
The Current Version: Minitab 16
Buy: Professional Bookstore
Rent: e5.onthehub.com
$29.99 to rent for 6 months,
$99.99 to own
Search: e5.onthehub.com minitab
0-17/17
Part 0: Introduction