1. Economics 20 - Prof. Anderson
Welcome to Economics 20
What is Econometrics?
Economics 20 - Prof. Anderson

2. Why study Econometrics?
Rare in economics (and many other areas without labs!) to have experimental data
Need to use nonexperimental, or observational, data to make inferences
Important to be able to apply economic theory to real world data
Economics 20 - Prof. Anderson

3. Why study Econometrics?
An empirical analysis uses data to test a theory or to estimate a relationship
A formal economic model can be tested
Theory may be ambiguous as to the effect of some policy change – can use econometrics to evaluate the program
Economics 20 - Prof. Anderson

4. Types of Data – Cross Sectional
Cross-sectional data is a random sample
Each observation is a new individual, firm, etc. with information at a point in time
If the data is not a random sample, we have a sample-selection problem
Economics 20 - Prof. Anderson

5. Economics 20 - Prof. Anderson
Types of Data – Panel
Can pool random cross sections and treat similar to a normal cross section. Will just need to account for time differences.
Can follow the same random individual observations over time – known as panel data or longitudinal data
Economics 20 - Prof. Anderson

6. Types of Data – Time Series
Time series data has a separate observation for each time period – e.g. stock prices
Since not a random sample, different problems to consider
Trends and seasonality will be important
Economics 20 - Prof. Anderson

7. The Question of Causality
Simply establishing a relationship between variables is rarely sufficient
Want to the effect to be considered causal
If we’ve truly controlled for enough other variables, then the estimated ceteris paribus effect can often be considered to be causal
Can be difficult to establish causality
Economics 20 - Prof. Anderson

8. Example: Returns to Education
A model of human capital investment implies getting more education should lead to higher earnings
In the simplest case, this implies an equation like
Economics 20 - Prof. Anderson

9. Economics 20 - Prof. Anderson
Example: (continued)
The estimate of b1, is the return to education, but can it be considered causal?
While the error term, u, includes other factors affecting earnings, want to control for as much as possible
Some things are still unobserved, which can be problematic
Use nlsy.dta to estimate a simple earnings function
Economics 20 - Prof. Anderson