This document provides an overview and outline of an introductory econometrics course. It discusses the course objectives, which are to develop a working knowledge of econometrics and its applications. The course will cover topics like simple regression, multiple regression, and time series analysis. Students will learn to conduct their own empirical research using econometric software. The assessment will include assignments, midterms, and a final exam.

1. ECO341K Introduction to Econometrics
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2. Lecture 1 - Outline
Course Outline
Course objectives
Textbook
Assessment
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3. Course Objectives
The aim of the course is to help you develop a working knowledge of
econometrics and its applications to real-world economic data.
The course will cover a range of topics:
− Simple Regression
− Multiple Regression
− estimation, inference
− extensions
− Learn a specialised econometric software package
By the end of session you will be able to:
=⇒ read and understand most analyses performed by econometricians
=⇒ conduct your own empirical research.
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4. Textbooks:
The required text is:
J.M. Wooldridge (2008) Introductory Econometrics: A Modern
Approach. 4th Edition
3rd edition is fine
A useful companion book:
J.M. Wooldridge (2008) Student Solution Manual for Introductory
Econometrics available electronically through the text website.
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5. Assessment
1. Weekly assignments (drop lowest) 10%
2. Two Mid-Term Exams 50%
3. Final Exam 40%
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6. Course Website (Blackboard)
This site will contain:
Lecture handouts (syllabus, etc)
Lecture notes for each class
Homework assignments, including data sets
Homework solutions
Data sets and STATA logs for in class examples
Special announcements (sent via email also)
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7. Topics covered – rough timeline
Topic Classes Approx.
Introduction 2
Simple Linear Regression 3
Multiple Linear Regression 5
Small Sample Inference 2
Large Sample Inference 1
Further Issues including Dummies 4
Time Series 2
Panel Data 2
Qualitative Response 2
Endogenous Regressors and IV 2
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8. Software – STATA
It is crucial that you have access to STATA and that you do the
empirical exercises
Access Options:
1 Timeshare through the web from anywhere
2 Purchase through STATA gradplan – see syllabus – about $100
3 Labs in Burdine?
What does STATA look like? Lets see!
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9. Lecture 2 - Outline
The Nature of Econometrics
What is Econometrics ?
The Structure of Economic Data
Causality ‘Ceteris Paribus’ and correlation
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10. What is Econometrics ?
Econometrics concerns the use of statistical methods in:
estimating economic relationships
testing economic theory
evaluating government and business policy.
forecasting and prediction
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11. Applications
Econometrics has many wider applications, for example
1 the effect of class size or spending on student performance
2 the effect of education on wages
3 testing for discrimination in labour and credit markets
4 the effect of minimum wages on unemployment
5 the effect of CEO compensation on firm performance
6 the effect of govt policies on inflation and economic growth
Common feature: Econometrics deals with nonexperimental data
drawn from observing economic events (the data are not collected
through controlled experiments in a laboratory).
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12. Conducting Empirical Economic Analysis
Econometrics is used in every branch of applied economics
Empirical Analysis uses data to test the predictions of a theory or
estimate a relationship.
An empirical analysis generally consists of
1 An economic model - which may be formally developed (e.g. derivation
of consumer demand equations from a model of utility maximisation)
or based on intuitive reasoning.
2 An econometric model - which requires specifying the nature of the
relationship between variables
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13. Conducting Empirical Economic Analysis
Example of an econometric model – a multiple regression model:
wage = β 0 + β 1 .educ + β 2 .exper + υ
where:
wage = hourly wage rate
educ = years of education
exper = years of employment
β 0 , β 1 , β 2 = parameters which describe the direction and strength of the
relationship between the wage and the factors which determine it
υ = error term (or ‘disturbance term’) which contains unobservable factors
(innate ability, job characteristics,...)
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14. Conducting Empirical Economic Analysis
With this model a range of hypotheses can be stated in terms of the
unknown parameters ( β 0 , β 1 , β 2 ).
Empirical analysis requires data, and econometric methods are used to
estimate the parameters of the model and to formally test hypotheses
of interest. The model can also be used to make predictions.
Methods need to take into account the structure of the data
4 main data structures: cross-sectional data, time-series data, pooled
cross sections, panel data
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15. The Structure of Economic Data
A. Cross-Sectional Data
sample of individuals, households, firms, countries or other units
taken at a point in time (“snapshot”)
usually obtained by random sampling from the population (and the
sample is “representative”)
cross-sectional data are widely used in economics and other social
sciences. Very common in applied micro such as labor economics,
public economics, industrial organization, health economics
=⇒ this is the main data structure we will focus on
if randomly sampled, order of observations is unimportant
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16. Cross-Sectional Data
Examples of a cross-sectional data set:
(a) Data set on wages and other personal characteristics
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17. Cross-Sectional Data
(b) Data set on economic growth and country characteristics
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18. Time Series Data
B. Time Series Data
observations on a variable (or set of variables) over time
E.g. stock prices, cpi, gdp, crime rates.
The chronological ordering of observations is important
=⇒ observations cannot be assumed to be independent over time, most
economic time series are (strongly) related to their recent histories
=⇒ econometric model needs to take this into account
Data frequency is important, due to seasonal patterns (e.g. daily,
weekly, monthly, quarterly, annual)
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19. Time Series Data
Example: data on minimum wages
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20. Pooled Cross Sections
C. Pooled Cross Sections
some data sets have both cross-sectional and time series properties.
E.g. 2 cross-sectional family surveys in US
- one in 2000 recording income, expenditure, family size,...
- a new random sample in 2005, with same questions
=⇒ pool them to increase sample size
=⇒ no family is in the sample for the 2 years
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21. Pooled Cross Sections
Pooled cross-sections can be an effective way to analyse govt policies
(e.g. look at economic relationships before and after the policy was
introduced)
Pooled cross sections are also very useful for studying group dynamics
over time (e.g. how are average wages evolving for the group who
entered the labour market during the last recession; what determines
changes in median house prices in specific areas of US)
Can analyse like a standard cross-section, though need to allow for
changes in variables over time
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22. Pooled Cross Sections
Example: Two years of house prices
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23. Panel Data
D. Panel (or Longitudinal) Data
Consists of a time series for each cross-sectional unit
=⇒ follow the same individuals / firms etc. over time
Example: crime statistics at the city level – to study things like effect
of law enforcement or economic conditions on crime
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24. Panel Data
panel data has some important advantages over other data structures
(we can control for certain types of unobserved characteristics, and
can study lags in behaviour).
some important questions cannot be answered without panel data
=⇒ e.g. studying dynamics behaviour of individual units
briefly consider simple panel data methods
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25. Causal Effects, Ceteris Paribus and Correlation
Causality and Ceteris Paribus in Econometric Analysis
In most tests of economic theory, and for evaluating policy, the goal is
to infer a causal effect of one variable on another
Most propositions in economics are ‘ceteris paribus’ by nature
Example: the responsiveness of the demand for coffee to price -
holding all other factors constant (such as income, prices of other
goods). If these other factors are not constant, we cannot determine
the casual effect of a price change on quantity demanded
not feasible to literally hold ‘all else equal’ .... but have enough other
factors been held constant to infer causality ?
properly applied, econometric methods can simulate a ceteris paribus
experiment
(⇒) economic theory and econometrics together can help us uncover
causal effects.
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26. Causality and Correlation
We may recall (hopefully!) from Prob/Stats ECO329 the concept of
correlation and covariance
Measures of linear association between 2 variables
Example: Education and Wages.
Do people with higher levels of education tend to have higher wages?
Do people with higher wages have more education?
Correlation is a measure of this assocation
Let r be the correlation between wage for person i - say yi and
education xi
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27. Recall (?????) that,
∑n=1 (xi − x )(yi − y )
i ¯ ¯
r= n n
∑i =1 (xi − x )2 ∑i =1 (yi − y )2
¯ ¯
r > 0 means that large y are associated with large x
r < 0 means that large y are associated with small x
r = 0 means no linear association
could be nonlinear
could be no association at all
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28. Keep in Mind
One cannot conclude causation by simply looking at correlation
Note r is symmetric in x and y so:
does x cause y
does y cause x
Even if one thought it went a particular direction there may be other
mitigating factors that need to be taken into account
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29. Examples:
Wages and Education are correlated (as we will see)
Which direction is plausible and why?
Other factors?
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30. Examples:
Sports
In watching football I often hear/see statements that are of the form:
“When team x (insert your favorite) runs the ball more than 30 times they
win 80% of the games but when they run less than 30 times they only win
30% of the games.”
What the heck does this mean? Clearly it looks like there is a
correlation between number of running plays and the chance of
winning.
But is it a causal effect?
If it were causal then it would mean the coach could just make sure
he runs the ball at least 30 times (regardless) and will win more often
Is this how it works?
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31. Examples:
In the popular press there are many instances of people trying to infer a
causal relationship between variables based simply on correlations between
two variables.
Try and listen for examples of this.
Now lets play with some data!
1 Wage data – relation between education and wages
2 Test score data – relation between class size and standardized test
scores
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