econometrics stata assignment

ECON 203: Econometrics

Stata @ wellesley, organizing your data, helpful books.

• Find Data Using IPUMS
• Find Supporting Literature
• Cite Sources
• Class Activities - 203-02
• Class Activities - 203-03

Stata is available for installation on student computers: PCs | Macs

HERE are instructions for installing Stata.

Stata is available on all classroom and lab computers on campus.

Want to use Stata off-campus? Install it on your laptop (while on campus), and then use the VPN to log in and access Stata off campus. Visit the VPN help page  for instructions.

For issues installing Stata or setting up the VPN, visit the Help Desk in Clapp Library or email [email protected]

Organizing Folders

• Create a new folder for your class. Name it without spaces (e.g., ECON203-SP12).
• Create 2 new folders in your class folder: one for assignments (Assignments) and one for your final project (Project).
• Save your data files to the appropriate folders within your class folder.

Naming Files

-Adopt consistent file naming conventions!

-Name your files something that alludes to their content or purpose.

Example 1: Data for a class's second lab. Poor choice: mydata.dta Better name: lab2census.dta

Example 2: You download US census data for your final project for the year 2000; name it census00.dta. Then you create a subset containing only women ages 18-30; name it census00fem.dta.

-If you have multiple versions of the same file, add increasing numbers to the end of the file (e.g., census00v2, census00v3, etc). If you go back and make changes to an earlier version of the file, save a copy with the next highest number (e.g., census00v4).

Answers to the Top Stata Questions at Wellesley

• Help! My Dataset Won’t Open!
• How do I create dummy variables? How do I recode variables?
• How do I combine two datasets?
• When I run a regression, why do some of the variables and/or observations disappear from the output?
• How do I run regressions with fixed effects?
• How to Download World Bank Data and Open It in Stata

Converting Data to Stata Format

Use these tutorials to help you get your data into Stata.

Need to open other file formats in Stata? Contact Carolin for help!

• From SAS or SPSS Format (.sas7bdat, .sd7, .sd2, .ssd01, .xpt, or .sav)
• From Raw (.dat, .raw) Format
• From Excel (.xls, .xlsx) or basic tables (.txt, .csv)

Saving Output Tables

Log Files - keep track of every command you run and all the results. Great way to keep a record of what you have done.

• To start a new log: First change your working directory to the correct folder ( cd command). Then in the command box type log using "logname", text . Give your log file a usefulname such as 'Lab2Feb6.log.' Begin a new log file each time you begin working in Stata or use the append option to add on to an existing log file.
• To view an existing log: You can open the file in any text editor or in Stata (type view logname.log ). This is helpful for remembering what you did from one session to the next.

Copy & Paste - quick way to copy results to a document or spreadsheet

You can highlight the tables in Stata's Results window and copy the contents. Rather than using CTRL+C, right-click on the highlighted text to get additional options:

• Copy Text - same as CTRL+C. Not recommended for pasting tables into Excel/Word.
• Copy Table - Aligns cells by tabs. Works well when pasting into  Excel/Word.
• Copy Table as HTML - Copies an HTML table, which pastes well into Word.
• Copy Table as Picture - makes a screenshot of the table. Can't edit the picture without a graphic program. Best for quickly pasting into Word.

Opening IPUMS Data in Stata

• How to Download IPUMS Data and Open it in Stata

Help with Stata

via Email Email [email protected] with your questions!

ECON 203 Stata Cheat Sheet

• ECON 203 Stata Cheat Sheet Cheat sheet on Stata commands for students in ECON 203.

Other Helpful Websites for Learning Stata

• Stata Help Guides (Princeton) Includes introductory and more advanced topics, including data manipulation and statistical analysis in Stata.
• Resources to Help You Learn & Use Stata (UCLA) Includes a 'Stata Starter Kit', helpful examples with annotated output, and a quick reference guide for choosing the appropriate statistical test.
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• Last Updated: Apr 22, 2024 3:33 PM
• URL: https://libguides.wellesley.edu/econ203

Statisticshelpdesk Blog Posts

• Step-By-Step Multivariate Regression For Econometrics Assignments: A Helpful STATA Guide

Multivariate regression is also known as a system of regression equations. We use it to identify and understand the relationship between multiple variables. This essential statistical method is useful when it comes to estimating housing prices, the effects of economic policies, or general market trends. Multivariate regression is an essential tool in econometrics; it helps to determine how changes in several independent variables influence one dependent variable. This technique is particularly useful in analyzing intricate economic events and making decisions based on data. In this guide, We will explain how to conduct multivariate regression using STATA and  outline how students taking econometrics courses can benefit from econometrics assignment help services in order to learn the techniques to solve complex econometric problems..

What is a multivariate regression?

Multivariate regression is an extension of simple linear regression. Instead of just one independent variable (X) explaining one dependent variable (Y), you can have multiple independent variables (X1, X2, X3, etc.) working together to explain Y. This allows you to model intricate real-world scenarios in which multiple factors influence the outcome. For instance, housing prices might depend on square footage (X1), number of bedrooms (X2), neighborhood (X3), and so on.

Why use Stata for multivariate regression?

Stata is very flexible software for working with data and contains a wide range of commands for econometric analysis. Here’s why Stata is preferred for multivariate regression:

User-Friendly Interface: STATA is an exceptional tool because of its user-friendly interface and powerful functions. Speaking of menus and commands, they are very user-friendly, which enhances the experience of using Stata and simplifies its usage.

Robust Analytical Tools: Stata provides econometric and statistical tools to conduct complex analysis using big data. These tools are crucial when performing comprehensive and efficient multivariate regressions.

Extensive Documentation and Support: Stata has extensive documentation that covers every possible aspect of the program to use any of the functions provided. Also, it has a dedicated communities of users and enthusiasts with ample discussions and solutions to the various problems associated with the application of STATA. This guarantees that you can always find assistance when you encounter difficulties or require clarification on a particular function or method. These elements, together with detailed manuals, help users solve problems and improve their knowledge of the software.

Steps for conducting Multivariate Regression in Stata

Step 1: Load Your Data

If you have the data set handy, the first step is to loading the data into Stata. This can be done using the use command.

use “your_dataset.dta ” , clear

Replace “ your_dataset.dta” with the path and filename of your dataset.

Step 2: Specify Your Regression Model

To specify a regression model, use the regress command followed by the dependent variable(s) and independent variable(s). Here is the syntax:

regress depvar1 depvar2 indepvar1 indepvar2 ...

Step 3: Interpret the Results

Stata will now display the output of the regression, along with coefficients, standard errors, significance levels (p-values), and other important data. It is very important to study the output carefully to understand the relation among variables.

Step 4: Saving Output:

To use the output from your regression results for subsequent analysis and inclusion in the report, we can use the outreg2 command or simply copy-paste from Stata’s results window into a text file or document.

Using mvreg Command for Multivariate Regression

The mvreg command in Stata is generally applied when we have multiple dependent variables (hence multivariate) and want to model them simultaneously. It allows to specify a set of explanatory variables (independent variables) that influence multiple outcomes.

mvreg depvarlist = indepvarlist

• depvarlist: List of dependent variables separated by spaces.
• indepvarlist: List of independent variables separated by spaces.

Using manova Command for Multivariate Analysis of Variance

On the other hand, the manova command is used for multivariate analysis of variance (MANOVA), which tests the equality of means for multiple dependent variables across different groups. It is not typically used for multivariate regression where you predict continuous outcomes based on explanatory variables.

manova depvarlist = indepvar, by(groupvar)

• indepvar: Independent variable (typically categorical) affecting the dependent variables.
• groupvar: Grouping variable for different levels of the independent variable.

Econometrics Assignment Help: Your Trusted Partner

We understand that econometrics assignments can be quite challenging, especially when dealing with software such as Stata. For econometrics students, we provide econometrics homework help to make these challenges more manageable and empower students to learn the basics of econometrics using STATA.

• Stata Expertise: Our team comprises econometrics experts proficient in Stata, and therefore we aim to provide top-quality services to our clients. It reduces the possibilities of errors in handling large volume of data and guarantees accurate results.
• Customized Solutions: Our tutors solve assignments according to the rubric and instructions. As mentioned earlier, our approach is personalized and, thus we engage with students on a one-to-one basis to assess their level of knowledge and provide assistance accordingly. This  approach makes it easy for our students to learn the concepts of econometrics at their own pace.
• Data Analysis and Interpretation: We help students in learning the cleaning process of raw data and prepare it for analysis using Stata or for that matter any statistical software. You not only get your homework done but also understand the steps performed in stata that are explained by our experts in order to generate the results.
• Model Building and Diagnostics: Choosing the correct models, deciding upon parameters, and doing diagnostic tests can be quite a challenge. We assist you with these tasks, including helping you build your model, and reviewing your diagnostics to ensure the effectiveness of your analyses.
• Code Review and Feedback: For practicing, we provide stata do-files containing the codes that you can run at your end to generate the results. We also provide assistance with reviewing the codes and correcting the errors to get the desired outcome. You may also seek feedback on your report for amendments and improvments.

Why Choose Our Econometrics Assignment Help?

• Expert Guidance: Learn from experienced econometricians who are passionate about teaching and helping students succeed. Their expertise ensures you receive the best possible support at the time of need.
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• Confidentiality: We prioritize the privacy and security of your data and information. Your trust is important to us, and we ensure that all your details are handled with the utmost care and confidentiality.

Resources for Econometrics Students

• “ Introductory Econometrics: A Modern Approach” by Jeffrey M. Wooldridge
• “ Basic Econometrics” by Damodar N. Gujarati and Dawn C. Porter

Stata Resources:

• Stata Manual
• “ Statisticshelpdesk.com” a leading website providing Stata homework assistance. 

We provide professional guidance and expert instruction, tailored to your individual needs. Our team of experts is composed of experienced economists, statisticians, and other specialists. We are here to help you reach your academic goals.

18-Jun-2024 12:28:00 | Written by Salvador

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Princeton University Library

Spi 508a: econometrics and public policy.

• Library Databases
• STATA Resources
• Stokes Viz Hub workshops
• Major Guides to Statistics and Data
• Citing Sources

Data and Statistical Services (DSS)

Data and Statistical Services (DSS) provides data and statistical consulting. The service is located in Firestone Library.

Experts are available to advise Princeton University student, faculty, and staff on choosing appropriate data, application of quantitative research methods, the interpretation of statistical analyses, data conversion, and data visualization. Subject specialists help choose appropriate data. The statistical packages supported by consultants are R/R Studio, Stata, and SPSS. We provide statistical and software assistance in quantitative analysis of electronic data as part of independent research projects, such as junior papers, senior theses, term papers, dissertations, and scholarly articles.*

* Please note: Students are welcome to use our online tutorials and computing services. DSS consultants, however, do not provide assistance with homework assignments, problem sets, or take-home exams.

Library Resources to Connect Econometrics with Stata code

• 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.

Relevant STATA books from the PUL catalog

Regression Discontinuity workshop: Data and Stata .do file

• Data file for Members of Parliament (MP) example
• Stata .do file for MP example
• Slides from 2/18 lecture
• << Previous: Library Databases
• Next: Stokes Viz Hub workshops >>
• Last Updated: Apr 16, 2024 11:35 AM
• URL: https://libguides.princeton.edu/SPI508A

Principedia

Econometrics Application

Description of course goals and curriculum.

Learning From Classroom Instruction

Learning For and From Assignments

External Resources

What Students Should Know About This Course For Purposes Of Course Selection

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• Department of Agricultural Economics and Economics
• Christiana Stoddard, Ph.D.
• ECNS 562: Econometrics 2

Econometrics 2

  stata resources, introductory resources.

• Some more common commands
• Time Series commands
• A few common panel data commands
• another stata example.do
• Official List of reference sites:  http://www.stata.com/links/resources1.html
• Learning Modules:  http://www.ats.ucla.edu/stat/stata/

More Advanced Resources

• All files and datasets that go with Cameron and TrivediMicroeconometrics
• Includes chapters on quantile regression, IV, Nonlinear models, Bootstrapping, maximum likelihood, panel data, and other methods
• Modules with datasets and commands in LIMDEP, STATA, and SAS for (1) data management and heteroskedasticity issues, (2) Endogenous regressors with natural experiments, instrumental variables, and two-state estimators, (3) panel data, and (4) sample selection issues.

Homework Assignments

• WOOLDRIDGE  Data  for homework
• DATA  Codebook

Lecture Notes

CAUTION:   These are based on my own personal notes, and as such probably have errors, things that make sense to me but do not make sense without the accompanying lecture, tables and figures pulled from other papers that should not be used without citing the originals, etc.  For personal use ONLY.  Not a substitute for class or for reading the text.

• Power point slides with state and year fixed effects and state specific trends (your handouts were based on this )
• Summary notes RD
• Summary notes for heteroskedasticity
• Summary notes for autocorrelation
• Summary notes for Chapter 13
• Summary notes for Chapter 14
• Summary notes chapter 15
• Summary notes chapter 16
• Summary notes chpater 17

Readings—Accessible from MSU domain

Christiana Stoddard, Ph.D. Department of Agricultural Economics and Economics Montana State University P.O. Box 172920 Bozeman, MT 59717-2920

Tel: (406) 994-5634 Fax: (406) 994-4838 E-mail: [email protected] Location: 307D Linfield Hall

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Comment from the Stata technical group

Econometrics for Dummies is an ideal companion for an introductory course in econometrics. The book is written for people that want to learn how to use econometrics in their research and complements the discussion of theory with advice about how to move from data and economic theory to estimation. All the computational examples and output in the book use Stata. The book assumes some previous knowledge of statistics and economics but does offer a comprehensive review of the basic concepts needed to understand the concepts in the text.

The first part of the book is a review of basic statistics and probability, an introduction to Stata, and a discussion of the different types of data commonly encountered by researchers. The book then delves into the ordinary least-squares and the Gauss-Markov theorems. After presenting the Gauss-Markov theorem the author discusses the most common violations of the assumptions of the theorem — heteroskedasticity, collinearity, and autocorrelation — and how to diagnose and deal with them. The book also discusses binary outcome models, models for censored and truncated outcomes, sample selection, time-series models, and panel-data models.

Econometrics for Dummies presents theoretical econometric results and provides an intuitive interpretation of them. The book is a good reference for those wanting to get an insight into basic econometric concepts encountered in an introductory econometrics course.

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Review from the Stata Journal Chinese and Russian translations available

Comment from the Stata technical group

An Introduction to Modern Econometrics Using Stata , by Christopher F. Baum, successfully bridges the gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.

The first three chapters are dedicated to the basic skills needed to effectively use Stata: loading data into Stata; using commands like generate and replace , egen , and sort to manipulate variables; taking advantage of loops to automate tasks; and creating new datasets by using merge and append . Baum succinctly yet thoroughly covers the elements of Stata that a user must learn to become proficient, providing many examples along the way.

Chapter 4 begins the core econometric material of the book and covers the multiple linear regression model, including efficiency of the ordinary least-squares estimator, interpreting the output from regress , and point and interval prediction. The chapter covers both linear and nonlinear Wald tests, as well as constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models.

Chapters 5 and 6 focus on consequences of failures of the linear regression model’s assumptions. Chapter 5 addresses topics like omitted-variable bias, misspecification of functional form, and outlier detection. Chapter 6 is dedicated to non-independently and identically distributed errors, and it introduces the Newey–West and Huber/White covariance matrices, as well as feasible generalized least-squares estimation in the presence of heteroskedasticity or serial correlation. Chapter 7 is dedicated to the use of indicator variables and interaction effects.

Instrumental-variables estimation has been an active area of research in econometrics, and chapter 8 commendably addresses issues like weak instruments, underidentification, and generalized method-of-moments estimation. In this chapter, Baum extensively uses his wildly popular ivreg2 command.

The last two chapters briefly introduce panel-data analysis and discrete and limited-dependent variables. Two appendices detail importing data into Stata and Stata programming. As in all chapters, Baum presents many Stata examples.

An Introduction to Modern Econometrics Using Stata can serve as a supplementary text in both undergraduate- and graduate-level econometrics courses, and the book’s examples will help students quickly become proficient in Stata. The book is also useful to economists and businesspeople wanting to learn Stata by using practical examples.

About the author

Christopher F. Baum is an economist at Boston College, where he codirects the undergraduate minor in scientific computation. He is an associate editor of the Stata Journal and co-organizer of Stata Users Group meetings in Boston. Baum has coauthored many Stata routines and maintains the Statistical Software Components Archive of downloadable Stata components. He has taught econometrics at the undergraduate and graduate levels, making extensive use of Stata, for many years.

Comments from readers

This book provides an excellent resource for both teaching and learning modern microeconometric practice, using the most popular software package in this area. The coverage includes discrete choice models and models for panel data, as well as linear regression and instrumental variables methods. I particularly like the material on handling large datasets and developing efficient programs within Stata, which provide the reader with an invaluable introduction to good practice in empirical research.

Prof. Steve Bond Nuffield College, Oxford and Institute for Fiscal Studies (IFS) London

Kit Baum provides students and researchers a hands-on guide to modern econometric techniques by means of many well-documented examples in Stata. The examples are also useful templates for those who need to write Stata routines for their own work. Treatment and transformation of cross-section, time-series, and panel data are carefully explained. The coverage of the text is broad and up to date. An Introduction to Modern Econometrics Using Stata is a valuable companion to undergraduate- and graduate-level econometric textbooks.

Serena Ng Department of Economics, University of Michigan

Christopher Baum’s An Introduction to Modern Econometrics Using Stata is probably the only econometrics text published to date that pays serious attention to reproducibility of research and systematic data validation using Stata’s data audit commands along with do-file and programming capabilities. Economic and financial consultants will find this text to be an invaluable guide to using Stata for creating reproducible, error-free data and econometric analysis, as well as quality graphic presentations. The book is comprehensive and easy to follow, with substantive coverage of econometric theory and applications using the full array of Stata’s capabilities. This text should serve as an excellent learning and reference guide for every consultant.

Zaur Rzakhanov, Ph.D. Associate, Analysis Group Inc. Boston, Massachusetts

This book is a wonderful complement to the Stata technical manuals. It provides a wealth of practical tips and sample applications that help the intermediate-level Stata user advance in making the most efficient use of Stata. It is thoughtfully organized along the lines of an econometrics textbook, allowing practitioners to find relevant and useful commands, procedures, and examples by topics that are familiar and immediate. It also includes a most helpful appendix for novice programmers that will expedite their development into proficient Stata programmers. This book is a must-have reference for any organization that needs to train practitioners of econometrics in the use of Stata.

Peter Boberg CRA International

For too long there has been a hole in the field between econometrics textbooks, which focus on theory but give little practical guidance to the day-to-day realities of economic research, and software manuals, which provide detail but little analytical context. Researchers, analysts, and students have no single source to turn to and often waste valuable time and effort reinventing the wheel. This book brings it all together and gives the researcher a huge step up on the learning curve. It perhaps should have been subtitled “How to perform high-quality empirical research using Stata.” It addresses topics in the order that real-world research is performed, beginning with the data-management and quality-control issues that a researcher must contend with every day and then proceeding to the econometric tools used for most empirical analyses. A researcher or a research analyst reading this book would learn insights and tricks of the trade that would otherwise take years to accumulate. Common errors (such as those resulting from many-to-many merges) are pointed out. Useful tips (such as the use of local macros) are discussed. Efficient and robust programming is encouraged throughout. This book should be required reading for any empirical researcher or research analyst interested in developing a high-quality research process.

Dr. Paul Liu The Brattle Group

Christopher F. Baum is an economist at Boston College, where he codirects the undergraduate minor in scientific computation. He is an associate editor of the Stata Journal and co-organizer of Stata Users Group meetings in Boston. Baum has coauthored many Stata routines and maintains the Statistical Software Components archive of downloadable Stata packages. He has taught econometrics at the undergraduate and graduate levels, making extensive use of Stata, for many years.

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