Empirical relationships
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Empirical Relationships. Lecture 1. Today’s Plan. Syllabus & housekeeping issues Course overview What is econometrics? Two econometric examples. Teaching Team. Professor: Andrew K. G. Hildreth 515 Evans Hall (510) 643-0715 [email protected]

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Today s plan
Today’s Plan

  • Syllabus & housekeeping issues

  • Course overview

    • What is econometrics?

    • Two econometric examples

Teaching team
Teaching Team

Professor: Andrew K. G. Hildreth

515 Evans Hall (510) 643-0715

[email protected]

Office Hours: Monday 2-3 pm & Wednesday 10-11am

Assistant: Judi Chan, (510) 643-1625 [email protected]


Brachet Tanguy: [email protected]

Office Hours: location and time to be advised. Sections 105 & 106.

Francisco ‘Paco’ Martorell: [email protected]

Office Hours: location and time to be advised. Sections 101 & 102.

Sally Kwak: [email protected]

Office Hours: Tues & Thurs 12.30-2pm 508-5 Evans. Sections 103 & 104.

Course website
Course Website

  • emlab.berkeley.edu/users/hildreth/e140_sp02/e140.html

  • What you’ll find at the website:

    • My picture

    • Excel files

    • Lecture notes

    • Problem Sets (& Solutions)

    • Midterms (after the tests) & Solutions

    • Supplemental handouts

What is econometrics
What is Econometrics?

  • Broadly defined: the study of economics using statistical methods

  • Founding members of the econometric society described it:

    “..as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference.” --Samuelson, P., Koopmans, T. & Stone, R. Report of the Evaluative Committee for Econometrica, Econometrica, 1954, p. 142

Why econometrics
Why Econometrics?

  • When we read the newspaper or see announcements of economic statistics or predictions, how are the stats and predictions derived?

  • Some uses:

    • Returns from investing in 1 more year of school

    • 2000 Florida election

    • Macroeconomic indicators

    • Production function estimates


  • Econometrics is a doing subject!

  • It is an art that must be learned through practice - working out problems algebraically, using economic data, building models using computer software

  • No one exact way to present a statistical argument

  • Course objective: providing you with knowledge of econometrics in theory and application

  • Vocational uses

    • consultancy

    • business planning

    • politics or public policy

    • lawyers, circuit court judge, Supreme Court judge

Returns to education
Returns to Education

  • Examining relationship between years of education and earnings using Gary S. Becker’s 1964 theory on human capital

  • Comparing the cost and future returns of an additional year of schooling

    • Future earnings are function of schooling given by:

      W=f (s) where s = given # years of schooling

    • But there’s a simultaneity problem: do you earn more because you have more schooling or do you pursue more schooling to earn higher wages?

Returns to education 2
Returns to Education (2)

  • Test the relationship using cross-section data from Current Population Surveys (CPS) for CA males in 1979 and 1995

  • You can use the 1995 data to graph gross weekly earnings vs. years of schooling, but it’s impossible to see any relationships between earnings and years of schooling

  • The same goes for the 1979 data - it’s a mess!

  • To highlight an array in EXCEL, hold CTRL+SHIFT and press the down arrow

Returns to education 3
Returns to Education (3)

  • Use conditional means to get a better approximation of the earnings and education relationship

  • Conditional mean: the mean value of a variable Y given the value of another variable X

    • General formula:

    • In our case:

      Wi= gross weekly earnings

      S = years of schooling

Returns to education 4
Returns to Education (4)

  • Using conditional means, you can compare the mean gross weekly earnings associated with different years of schooling - the graph is less messy

  • There may be problems with our analysis !

    • definitions of schooling changed

    • boundary set for top coding changed: in 1979, it was $999. In 1995 it was $1923

    • Macro and microeconomic factors

Chasing butterflies
Chasing Butterflies

  • What happened in Palm Beach, Florida during the 2000 election?

  • Can we test the assertion that the butterfly ballot confused voters and caused them to accidentally vote for Buchanan rather than Gore?

  • If Palm Beach County hadn’t used the butterfly ballot, can we show that Gore would have won Florida?

  • The course website has Excel datasets of voting outcomes in Broward County, Palm Beach County, and Florida.

Chasing butterflies 2
Chasing Butterflies (2)

  • Broward County is similar to Palm Beach in size and demographics, but the butterfly ballot was unique to Palm Beach

  • Graphing the number of votes for Buchanan against those for Gore in Broward County, we see that he received less than 10 votes in any of the voting precincts

  • Looking at the same graph for Palm Beach, we see that Buchanan received many more votes there than he did in Broward County.

Chasing butterflies 3
Chasing Butterflies (3)

  • We can also look at the number of votes for a party vs. the number of registered voters for that party

  • We see a similar upward trend for Democrats and Republicans

  • However, for the Reform voters Palm Beach is an extreme outlier - for the other 66 counties, there were less than 1,000 Reform votes cast. Palm Beach County had 3,407 Reform votes cast!

Chasing butterflies 4
Chasing Butterflies (4)

  • You can use a confidence interval to test whether the Palm Beach observation is statistically different from the others

    • Regress the number of Reform votes on the number of registered Reform voters by county, not including Palm Beach

    • We find the coefficients are highly statistically significant

  • 95% confidence interval means that there is a 5% chance that an observation will lay outside that interval by error. Notice that Palm Beach doesn’t lie in that interval.

  • What degree of confidence do we need to include Palm Beach in the confidence interval?

Wrap up
Wrap up

  • An overview of what’s to come

  • An introduction to economic data and the idea of empirical relationships between two measured variables.

    • Example: years of education and gross earnings

  • Problems inherent in using economic data to test empirical relationships

  • Conditional mean function

  • Examining differences in data relationships

  • Other forms of data: time-series and its relation to cross-section data