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Current version: May 2019 (v4.1)*
This guide serves to assist MSc and PhD students in using Stata for empirical financial accounting research.
Stata is a powerful program that can be used to analyze many different research questions in accounting and finance. While there are many statistical packages out there (such as SPSS, or even Excel), the major advantage of Stata is that it allows you to carefully manage your data and research process, compute key variables needed in empirical research (e.g., fitted or residual values from a prediction model), and easily merge large sets of data (e.g., combining financial statement data from Compustat with stock market data from CRSP and analyst forecast data from IBES). The purpose of this guide is to give students a head start in using Stata in empirical accounting and finance research settings.
* The May 2019 edition contains small error corrections. Most importantly, it corrects a small (but consequential) error in the DREV variable calculation in Appendix A.1 on discretionary accruals estimation.