ECO 4412 ECONOMIC STATISTICS AND ECONOMETRICS - PowerPoint PPT Presentation

1 / 23
About This Presentation
Title:

ECO 4412 ECONOMIC STATISTICS AND ECONOMETRICS

Description:

Schedule. Chapter 4, 4 weeks. Chapter 5, 3 weeks. Chapter 6, 3 weeks. Chapter 7, 3 weeks ... total annual attendance at major league baseball games in 1970 on ... – PowerPoint PPT presentation

Number of Views:315
Avg rating:3.0/5.0
Slides: 24
Provided by: shelbyg
Category:

less

Transcript and Presenter's Notes

Title: ECO 4412 ECONOMIC STATISTICS AND ECONOMETRICS


1
ECO 4412ECONOMIC STATISTICS AND ECONOMETRICS
  • Fall 2005
  • 3 Credits
  • 130-245 TR
  • Prerequisites ECO 3401, ECO 3411

2
Shelby Gerking
  • CBAII 302M
  • 407.823.4729
  • Sgerking_at_bus.ucf.edu
  • Office Hours 245-400 pm TR and by appointment

3
Course Outline
  • Text Stock and Watson, Introduction to
    Econometrics, 2004
  • Goals Understanding how to use simple linear
    regression, multiple regression, dummy variables,
    hypothesis testing, panel data, and related
    issues and methods

4
Schedule
  • Chapter 4, 4 weeks
  • Chapter 5, 3 weeks
  • Chapter 6, 3 weeks
  • Chapter 7, 3 weeks
  • Chapter 8, 2 weeks
  • Chapter 9, time permitting

5
Grading
  • Two mid-term exams (15 each)
  • Final exam (15)
  • Four papers
  • Paper 1 (5)
  • Papers 2,3,4 (10 each)
  • Homework (20 altogether)
  • Final letter grades based on curve as explained
    at first class meeting

6
Papers
  • Designed to help you learn about econometric
    software (LIMDEP)
  • Require you to collect your own data
  • Give you a chance to put econometric theory we
    study into practice
  • Give you experience with making written
    presentations of regression analyses

7
Late Work
  • Homework and papers may not be turned in late
    except because of authorized university
    activities
  • Exams (including the final) must be taken at the
    scheduled time set aside for this purpose except
    because of authorized university activities

8
Academic Dishonesty
  • Also known as cheating, academic dishonesty will
    not be tolerated in this class. This means that
    you must turn in only your own work on homework
    and paper assignments and on exams. Students
    attempting to enhance their grade by representing
    the work of others as their own will be subject
    to penalties prescribed by CBA and UCF policy.

9
(No Transcript)
10
What Can We Learn?
  • Dont be afraid of complex subjects
  • Perform your analysis with complete objectivity
    and independence
  • State your conclusions as truthfully as possible,
    even though they may be provocative and even
    though they may not be what people want to hear
  • if it is not perfect, it is not good enough
  • Respect for the data and the statistical methods
    of analysis

11
Two-Variable Linear Model
  • Consider the linear model
  • Yi?0 ?1Xiui
  • In this model, Y refers to the dependent
    variable, X refers to the independent variable
    and u is an error term representing the
    collective effect of all variables other than X
    on Y.
  • General idea is that X is the cause and Y is
    the effect
  • ?0 and ?1 are unknown population parameters to be
    estimated

12
Known Variables and Unknown Parameters
  • In our model,
  • Yi?0 ?1Xiui
  • We will have data on X and Y and will use those
    data to compute estimates of the unknown
    population values of the constant term and slope
    coefficient
  • Estimates of these parameters made by ordinary
    least squares (OLS). This method described more
    fully later on.

13
Examples
  • Highway fatalities/stringency of drunk driving
    laws
  • Elementary students standardized test
    scores/class size
  • Annual earnings/years of schooling
  • Sales/Advertising
  • Quantity of a good demanded/Price
  • Consumption/Income

14
Numerical Example 1
  • Regression of total annual attendance at major
    league baseball games in 1970 on population of
    city where team is located
  • 23 (U.S.) teams in 1970, thus there are 23 pairs
    of observations (data points). Attendance and
    population measured in thousands of persons
  • OLS yields
  • Attendancei814.90.135x Populationiûi

15
Scatter Diagram
0.135?ATT? ?POP
.
Attendance
.
.
.
.
Estimated Regression Line
.
.
.
.
.
.
814.9
.
Population
16
Interpretation
  • Estimated slope coefficient0.135 means that if
    city population rises by one unit (1000 persons),
    attendance rises by 0.135x1000 persons (or 135
    persons).
  • Estimated constant term (814.9) determines the
    level of the regression line. Does not mean
    that attendance would be 814,900 if population
    were zero.
  • Predicted attendance of a team in a city of 8
    million is 814.9(0.135x8,000)x10001,894,000

17
Interpretation (continued)
  • Regression line is an average relationship
    (e.g., slope coefficient is an average across all
    23 cities) for 1970? Would this relationship hold
    in other years? 2005?
  • What variables are represented by ui in this
    regression?
  • Won-loss record
  • Average income of city residents
  • Price of tickets
  • Many other things

18
Numerical Example 2
  • How is attendance affected by won-loss record?
  • Regress attendance on games behind (GB) in the
    standings at the end of the season
  • The result is
  • Attendancei1565.98-20.41xGBi ûi
  • Note negative slope coefficient
  • Does the constant term have a natural
    interpretation in this regression? What is it?

19
Diagram of Regression
-20.41?Attendance/?Games Behind
Attendance
1565.98
Estimated Regression Line
Games Behind
20
Ordinary Least Squares
0.135?ATT? ?POP
.
Attendance
.
.
.
.
Estimated Regression Line
.
.
.
.
.
.
814.9
.
Population
21
OLS Residuals (Errors)
0.135?ATT/?POP
.
Attendance
Ûi0
.
.
.
.
Estimated Regression Line
.
.
.
.
.
.
814.9
.
ÛjPopulation
22
Least Squares Method
  • Minimize the sum of squared residuals (SSR)
  • SSR?û12û22 ûn2
  • We could think of minimizing SSR by successively
    choosing pairs of values for the constant term
    and slope coefficient until SSR is made as small
    as possible
  • Or, we could use differential calculus (which
    turns out to be a lot easier)

23
Estimated Parameters
  • When using OLS
  • Estimated slope coefficient equals the sample
    covariance between Y and X (Sxy) by the sample
    variance of X (Sx2)
  • Estimated constant term equals the sample mean of
    Y minus the estimated slope times the sample mean
    of X
Write a Comment
User Comments (0)
About PowerShow.com