B020842 - ECONOMETRICS LAB

English

The first part aims at making the student familiar with all three components of data analysis: data management, analysis, and graphics. The second part aims at developing programming skills.

Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata (Vol. 2). College Station, TX: Stata press.

The course aims at developing data analysis and programming skills. The course will expose the students to practical issues encountered when doing applied research. Some programming skills will be developed and reproduction of published papers attempted.

Descriptive statistics, basic statistical inference, linear and logistic regression.

Lab sessions combined with short reviews of statistical methods and excerices to be done by students

Lab exam (35%) + applied paper on a topic decided between the student and the instructor (65%)

Data analysis
 Stata basics
o File extensions and Stata components
o Creating reproducible and documented analyses
o Saving time and effort while working
o Finding, installing, and removing community-contributed extensions to Stata
o Customizing how Stata starts up and where it looks for files
 Data management
o Reading in datasets of various standard formats
o Labeling variables
o Generating new variables in an efficient fashion, including leading, lagging, generating statistics within groups
o Combining datasets by adding observations and by adding variables
o Reshaping datasets for repeated measurements
o Missing values
 Analysis
o Using basic statistical commands (tables, descriptive statistics)
o Linear and logistic regression (OLS, maximum likelihood estimation)
o Robust standard errors
o Longitudinal analyses (fixed versus random effects models; clustered standard errors)
o Instrumental variables (2SLS)
o Matching
o Making tables using results of Stata commands
o Using common postestimation commands (test hypotheses about linear or nonlinear combinations of coefficients, generate fitted values, examine marginal effects)
o Working with interactions and factor variables
 Graphics
o Making common, simple graphs
o Building up complex graphs
o Representing model estimates graphically
o (Not) Using the Graph Editor


Programming
- Loops and conditional statements to automate your workflow
- Random numbers generators and setting seeds
- Writing your own functions
- Monte Carlo simulations to test estimators’ properties and departures from ideal conditions and to test your own estimation program

Comparing Stata and R
- Introduction to R
- Main differences between R and Stata
- Calling R commands from within Stata and viceversa
- Comparing some commands in R and Stata