{"id":4767,"date":"2023-09-18T21:36:40","date_gmt":"2023-09-18T19:36:40","guid":{"rendered":"https:\/\/eaa-online.org\/arc\/?p=4767"},"modified":"2023-09-18T21:36:40","modified_gmt":"2023-09-18T19:36:40","slug":"new-version-introductory-guide-to-using-stata-in-empirical-financial-accounting-research","status":"publish","type":"post","link":"https:\/\/eaa-online.org\/arc\/blog\/2023\/09\/18\/new-version-introductory-guide-to-using-stata-in-empirical-financial-accounting-research\/","title":{"rendered":"New version: Introductory Guide to Using Stata in Empirical Financial Accounting Research"},"content":{"rendered":"

A new version of my Stata guide is now available and freely accessible via Github (including code and data examples). Please see https:\/\/github.com\/dveenman\/stataguide<\/a> or here<\/a> to go directly to the pdf.<\/p>\n

The objective of this guide is to assist BSc\/MSc students, PhD students, and junior researchers in using Stata for empirical archival research. Stata is a powerful program that can be used to analyze many different research questions in the fields of accounting, finance, economics, and beyond. While many statistical packages exist, a 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.<\/p>\n

Version history:<\/p>\n