Economics Statistical Services (ESS)

The Economics Statistical Services (ESS) unit provides data analysis assistance to students enrolled in the economics department working on Junior Independent Work (JIW), Senior Thesis, or dissertation work.

The ESS team can help with any aspect of what we called the Anatomy of Data Analysis: data collection, data preparation, data cleaning, data merge/append, data visualization, descriptive statistics, linear or non-linear model, panel data analysis, time series, model selection, output interpretation, data presentation, and/or issues related with the use of Stata or R/RStudio for data analysis.

There are a number of tutorials covering basic procedures in Stata in this link.

A quick guide:

Data preparation/descriptives

• If you are collecting your own data, make sure variables are in columns and cases in rows, see examples on page 3 in this document.
• To import Excel or *.csv data to Stata see page 20 in this document (Stata 17 can also import SPSS -extension *.sav or *.por- and SAS data -extension *.sas7bdat, *.xport, or *.xpt)
• To merge or append data, in this document.
• Descriptive statistics, page 22 in this document.
• To run frequencies, see page 23 in this document.
• To run crosstabulations see pages 25-27 in this document.
• Visualization, in this document.

Regression models

• For OLS regression, page 6 in this document.
• For a logit regression, page 4 in this document.
• For a logit regression (odds ratio), page 6 in this document.
• For marginal effects or predicted probabilities, in this document.
• Nice regression outputs in this document:

Panel data

• Cross-sectional time series data look like the example on page 2 in this document.
• In Stata, you need to first set the data as panel, see here page 5 in this document.
• After setting the data as panel you can run a fixed or random effects regression, see pages 19, and 27 in this document.

Time series

• Basic time series procedures (date conversion, lag operators) in this document.

The documentation above will be reviewed and updated in the following months and links will be updated as well. On its current format the tutorials help with most of the common tasks needed for data analysis.

If you have any questions contact Oscar Torres-Reyna at otorres@princeton.edu and will try to reply as soon as possible.

Note that if you need help with homework or problem sets please refer to your teaching assistants (AI).