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Understanding and Applying STATA in Quantitative Research and in Forecast Trend

• First Online: 13 October 2022

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• Phuong Do-Thi 3 &

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 444))

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This document focuses on the use of Stata software in research and data processing. Operations are performed on Stata 16. This document refers to how to use statements to perform descriptive statistics and correlation analysis, OLS regression, to serve the research needs of researchers. This is the ideal handbook for researchers who need to use Stata to perform analysis of variables from beginning. The focus of this document, in addition to focusing on familiarization with Stata, is understanding the nature of the dataset to perform descriptive statistics, correlation analysis, simple OLS regression analysis, are the modality approach of this document.

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Kohler, U., Kreuter, F.: Data Analysis Using Stata, p. 237. Stata Press (2005)

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Cameron, A.C., Trivedi, P.K.: Microeconometrics Using Stata, p. 88. Stata Press (2009)

Adkins, L.C., Hill, R.C.: Using Stata for Principles of Econometrics, 4th ed. Wiley (2011)

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FPT University, Hoa Lac High-tech Park, Km29, Thang Long Boulevard, Thach That, Ha Noi, Vietnam

Phuong Do-Thi

Applied Modelling and Quantitative Methods MA/MSc, Trent University, 1600 West Bank Drive, Peterborough, ON, K9L 0G2, Canada

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Software Engineering Programme, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

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Do-Thi, P., Do, I. (2022). Understanding and Applying STATA in Quantitative Research and in Forecast Trend. In: Abdul Karim, S.A. (eds) Intelligent Systems Modeling and Simulation II. Studies in Systems, Decision and Control, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04028-3_41

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DOI : https://doi.org/10.1007/978-3-031-04028-3_41

Published : 13 October 2022

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Introductory Guide to Using Stata in Empirical Financial Accounting Research

dveenman/stataguide

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This repo contains my Stata guide ( a pdf book ) and the code examples presented in Chapters 4-5 and the Appendices. For the examples in Chapter 4, the Data folder contains several datasets that can be used to replicate the examples.

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.

Version history:

• Version 5.0: September 2023
• Version 4.1: May 2019
• Version 4.0: January 2019
• Version 3.0: December 2013
• Version 2.0: December 2011
• Version 1.0: December 2010

New in version 5:

• Update to Stata 18;
• Publication of code on Github;
• Improved and expanded section on standard errors;
• Improved section of fixed effects estimation;
• Improved section on matching and added entropy balancing;
• Added section on robust regression estimators in Chapter 4;
• Added section on difference-in-differences estimators in Chapter 4;
• Added section on creating and formatting graphs in Chapter 4;
• Added a separate chapter on simulations and programming in Chapter 5;
• Added Appendix on the use of Stata for downloading of data from WRDS;
• Improved Appendix on implied cost of capital estimation;
• Stata 100.0%

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USING STATA FOR APPLIED RESEARCH: REVIEWING ITS CAPABILITIES

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Stata is a statistical software package we'll be using for much of this course. 1 Stata has a number of advantages over other currently available software. For example, Stata keeps all data in memory; this makes it very, very fast. Stata is also largely command–driven (as opposed to menu–driven); while this makes its learning curve a bit steeper, once you learn Stata you will find it is substantially faster, easier, and more flexible in use than its competitors.

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subscription prices are posted at www.stata.com/bookstore/stb.html. Previous Issues are available individually from StataCorp. See www.stata.com/bookstore/stbj.html for details. Submissions to the STB, including submissions to the supporting files (programs, datasets, and help files), are on a nonexclusive, free-use basis. In particular, the author grants to StataCorp the nonexclusive right to copyright and distribute the material in accordance with the Copyright Statement below. The author also grants to StataCorp the right to freely use the ideas, including communication of the ideas to other parties, even if the material is never published in the STB. Submissions should be addressed to the Editor. Submission guidelines can be obtained from either the editor or StataCorp. Copyright Statement. The Stata Technical Bulletin (STB) and the contents of the supporting files (programs, datasets, and help files) are copyright c by StataCorp. The contents of the supporting files (programs, ...

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• Learn About Simple Regression in Stata With Data From the Consolidated State Performance Report (2012–2013) This dataset is designed for teaching simple regression. The dataset is a subset of data derived from the Consolidated State Performance Report from the U.S. Department of Education. It looks specifically at freshman graduation rates and courses taught by qualified teachers across the 50 U.S. states plus the District of Columbia and Puerto Rico. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata. The Student Guide and data curation were done by the Odum Institute, and the How-to Guide was co-authored by Abigail-Kate Reid and Nick Allum.
• Learn About Logistic Regression in Stata With Data From the American National Election Study (2012) This dataset is designed for teaching logistic regression. The dataset is a subset of data derived from the 2012 American National Election Study, and the example tests whether reported vote choice in the 2012 U.S. Presidential election is predicted by several factors, including a respondent’s race/ethnicity and how they feel about the Democratic and Republican Parties. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.
• Learn About Logistic Regression in Stata With Data From the National Household Education Surveys Program, School Readiness Survey (2007) This dataset is designed for teaching logistics regression. The dataset is a subset of data derived from the 2007 School Readiness Survey, and the example examines whether or not young children know all or most of the letters of the alphabet, and whether that is predicted by their TV viewing, their age, and whether their parents read to them. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata. The Student Guide and data curation was done by the Odum Institute; the How-to Guide was coauthored by Abigail-Kate Reid and Nick Allum.
• Learn About Multiple Regression With Dummy Variables in Stata With Data From the General Social Survey (2012) This dataset is designed for teaching multiple regression with dummy variables. The dataset is a subset of data derived from the 2012 General Social Survey, and the example presents an analysis of whether a person’s weight is a linear function of a number of attributes, including whether the person is female and whether the person smokes cigarettes. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata. The Student Guide and data curation were done by the Odum Institute, and the How-to Guide was co-authored by Abigail-Kate Reid and Nick Allum.
• Learn About Ordered Probit in Stata With Data From the Cooperative Congressional Election Study (2012) This dataset is designed for teaching ordered probit. The dataset is a subset of data derived from the 2012 Cooperative Congressional Election Study (CCES), and the example presents an analysis of whether survey respondents believe that laws covering the sale of firearms should be more strict, kept as they are, or less strict. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata. The Student Guide and data curation was done by the Odum Institute; the How-to Guide was coauthored by Abigail-Kate Reid and Nick Allum.
• Case Study: Electoral Systems and Turnout: Evidence From a Regression Discontinuity Design (note: no code included) This case study will show you how to carry out a Regression Discontinuity Design by way of an example about voter turnout in elections. In 2002, municipalities in Poland were assigned to either a majoritarian or a proportional electoral system based on a population threshold of 20,000 inhabitants. This assignment by use of a population threshold introduces a discontinuity that splits practically identical municipalities into two groups, one group with a proportional electoral system and another group with a majoritarian electoral system. We will show you in this case study how to use this discontinuity to reliably and accurately estimate the effect of a change from a majoritarian to a proportional electoral system on voter turnout.

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An Introduction to Statistics and Data Analysis Using Stata , by Lisa Daniels and Nicholas Minot, is an excellent textbook for introducing statistics, research methods, and Stata to undergraduate students in social sciences, behavioral sciences, business, and similar disciplines. This book walks students through the entire research process from designing a study and collecting data to performing statistical analyses to reporting the results. The authors discuss descriptive statistics and graphics for describing data; hypothesis testing for one and two means; cross tabulations and tests for association; and linear, logistic, and probit regression.

The authors devote one of the early chapters to introducing Stata. Then, throughout the book, they demonstrate how to use Stata commands and Stata's point-and-click interface for each data management task, graph, and statistical analysis they discuss.

Daniels and Minot provide many resources for instructors and students. Instructors have access to suggested test and homework problems along with solutions. They can also take advantage of PowerPoint slides that correspond with each chapter in the book. Students have access to all datasets so that they can work along with examples in the book. They can also use the electronic flashcards provided to review the concepts discussed in each chapter. Drawing on over 20 years of experience teaching statistics to undergraduate students, the authors are careful to include everything you need for an introductory statistics course taught using Stata.

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