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Part 0: Introduction -1/17 Regression and Forecasting Models Professor William Greene Stern School of Business IOMS Department Department of Economics

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Regression and Forecasting Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 0 - Introduction. Professor William Greene; Economics and IOMS Departments Office: KMEC, 7-90 (Economics Department) - PowerPoint PPT Presentation

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Page 1: Regression and Forecasting Models

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Regression and Forecasting Models

Professor William GreeneStern School of Business

IOMS Department Department of Economics

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Regression and Forecasting Models

Part 0 - Introduction

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Professor William Greene; Economics and IOMS Departments

Office: KMEC, 7-90 (Economics Department) Office phone: 212-998-0876 Email: [email protected] URL: http://people.stern.nyu.edu/wgreene

http://people.stern.nyu.edu/wgreene/regression/Outline.htm

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Course Objectives

Basic understanding: The regression model as a framework for the analysis of relationships among variables

Technical know how: How to formulate a regression model, estimate its parameters, and understand the implications of the estimated model.

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We used McDonald’s Per Capita

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Macs and Movies

Countries and Some of the DataCode Pop(mm) per cap # of Language Income McDonalds1 Argentina 37 12090 173 Spanish2 Chile, 15 9110 70 Spanish3 Spain 39 19180 300 Spanish4 Mexico 98 8810 270 Spanish5 Germany 82 25010 1152 German6 Austria 8 26310 159 German7 Australia 19 25370 680 English8 UK 60 23550 1152 UK

Genres (MPAA)1=Drama2=Romance3=Comedy4=Action5=Fantasy6=Adventure7=Family8=Animated9=Thriller10=Mystery11=Science Fiction12=Horror13=Crime

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Movie Genres

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Movie Madness Data (n=2198)

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Case Study Using A Regression Model: A Huge Sports Contract

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.

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Baseball Data (Panel Data – 31 Teams, 17 Years)

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A Regression Model

team

1

Attendance(team,this year) = α + γ Attendance(team, last year) + β Wins (team,this year) 2

3

+ β Wins(team, last year) + All_Stars(team, this year)

+ (team, this year)

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= .54914

1 = 11093.7

2 = 2201.2

3 = 14593.5

Effect of 1 more win11093.7 2201.2= 32757

1 .59414Effect of adding an All Star

14593.5= 359571 .59414

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Marginal Value of an A Rod 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)

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Course Prerequisites Basic algebra. (Especially summation) Geometry (straight lines) Logs and exponents

NOTE: I (you) will use only base e (natural) logs, not base 10 (common) logs in this course.

Previous course in basic statistics – up to testing a hypothesis about a mean

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Course Materials

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

http://people.stern.nyu.edu/wgreene/regression/Outline.htm

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Course Software: MinitabThe Current Version: Minitab 16

Buy: Professional BookstoreRent: e5.onthehub.com $29.99 to rent for 6 months, $99.99 to own

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