Syllabus: ECOG 315/ECON 181 “Advanced Research Methods and Statistical Programming” (Summer 2025) - Syllabus: ECOG 315/ECON 181 “Advanced Research Methods and Statistical Programming” (Summer 2025)

This repository contains all of the course materials for ECOG 315/ECON 181 “Advanced Research Methods and Statistical Programming.” (Summer 2025)

NOTE: This syllabus is a live document. It will be periodically updated as the course progresses!

Overview¶

This is an in-person course, held on the Howard University Campus:

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Title	ECOG 315/ECON 181 “Advanced Research Methods and Statistical Programming”
Hours	Fridays 1:00pm-3:30pm (plus weekly 1-hr mentor meeting and asynchronous videos)
Dates	May 30, 2025 ~ July 18, 2025 (See below for schedule.)
Location	TBD
Office Hours	TBD

This course will be conducted by your instructors:

Instructor: Chris Carroll ccarroll@jhu.edu
Instructor: Matt White mnwhite@gmail.com
Instructor: Alan Lujan alujan@jhu.edu

Useful Resources¶

Duke Writing in Economics: Economics Research Paper Writing Guide: Here
Github Cheat Sheet: Here

Instructor—Chris Carroll¶

Contact¶

Please contact me about this course at my e-mail address: ccarroll@jhu.edu. His Github handle is llorracc.

Office Hours¶

Office hours TBD.

Bio¶

I am a professor of economics at Johns Hopkins University and co-chair of the National Bureau of Economic Research’s working group on the Aggregate Implications of Microeconomic Consumption Behavior. Originally from Knoxville, Tennessee, I received my A.B. in Economics from Harvard University in 1986 and a Ph.D. from the Massachusetts Institute of Technology in 1990. After graduating from M.I.T., I worked at the Federal Reserve Board in Washington DC, where I prepared forecasts for consumer expenditure. I moved to Johns Hopkins University in 1995 and also spent 1997-98 working at the Council of Economic Advisors in Washington, where I analyzed Social Security reform proposals, tax and pension policy, and bankruptcy reform. Aside from my current work at Hopkins and the NBER, I am also an associate editor at the Review of Economics and Statistics (ReStat), the Journal of Business and Economic Statistics (JBES), and the Berkeley Electronic Journal of Macroeconomics (BEJM).

Instructor—Matt White¶

Contact¶

Please contact Matt about this course at his e-mail address: mnwhite@gmail.com. His Github handle is mnwhite.

Office Hours¶

Please contact me by email to schedule a meeting on Zoom-- Matt is very available to meet!

Bio¶

I am an economist at Econ-ARK, a non-profit developer of open source computing tools for structural economic research. After growing up in New Hampshire, I did my undergraduate studies at Cornell University, and then pursued graduate study in economics at Johns Hopkins University. After receiving my PhD, I was an Assistant Professor of Economics at the University of Delaware from 2013 to 2023, and held a Visiting Scholar position at the Consumer Financial Protection Bureau in 2015-16. My research spans heterogeneous agents macroeconomics and health economics, with projects examining the distributional impact of health insurance reform policies. I also develop new methods for solving dynamic stochastic optimization problems, and for efficiently representing them numerically. This work includes being the original designer and programmer for HARK, the Heterogeneous Agents Resources and toolKit.

Instructor—Alan Lujan¶

Contact¶

Please contact Alan about this course at his e-mail address: alujan@jhu.edu. His Github handle is alanlujan91.

Office Hours¶

Office hours TBD.

Bio¶

At Johns Hopkins University, Alan Lujan is a Program Coordinator and lecturer for the MS in Applied Economics program. He is a computational economist specializing in quantitative macroeconomics and structural econometrics, with particular interests in household finance and inequality. Lujan received his PhD in Economics from The Ohio State University in 2023 and subsequently joined the JHU Department of Economics as a Visiting Assistant Research Professor before his current role. His research integrates economic theory and computational techniques to better understand household financial behavior and its implications for inequality.

Course Materials¶

Course materials will be posted to the Canvas site for the course as well as to this GitHub repository. You should receive email notifications about Canvas uploads, and subscribing to

Course Description¶

This course combines two essential components for modern economic research: advanced research methods and statistical programming, applied towards the completion of a substantive term paper.

Econometrics and Machine Learning with Python (Zoom on demand): This part focuses on the practical implementation of econometric techniques and machine learning tools using Python, essential for rigorous empirical analysis. This session will be held virtually via Zoom (link TBD).
Tools for Writing a Research Paper in Economics (Afternoons, 1:30pm - 4:00pm): Building on a topic selected in the first week of the course, this part guides students through the entire research process live and interactively. We will cover formulating a question, exploring relevant tools (like Google Scholar, ChatGPT, Wikipedia, LitMaps, PaperPile), structuring the paper, creating content (using Jupyter notebooks), managing the project (with GitHub), generating and incorporating figures and tables, and preparing slide presentations for presenting findings. This session will be held in-person.

Throughout the semester, each student (or student pair) will develop a research paper on their chosen topic. The course is hands-on, requiring laptops for interactive sessions (both virtual and in-person). Writing assignments will primarily take the form of Jupyter notebooks (or LaTeX for graduate students).

Mandatory Mentoring: In addition to class time, each student is required to schedule and attend a one-hour meeting each week with an assigned economist mentor to discuss their term paper progress. It is the student’s responsibility to arrange this meeting at a mutually convenient time during the week.

Course Requirements¶

Recommended Course Background: some familiarity with Python or other modern programming languages (though having taken a formal course in such a language is not required).

Schedule¶

Note: Class times/days vary. Please check carefully.

#	Date	Phase	Due
1	Fri May 30	0. the setup	see this class’ agenda
2	Fri Jun 6	1. the pitch	see this class’ agenda
3	Tue Jun 10	1. the pitch	dry run for pitches
4	Fri Jun 13	2. the draft	research pitches (in-class)
5	Fri Jun 20	2. the draft	see this class’ agenda
6	Fri Jun 27	2. the draft	see this class’ agenda
7	Thu Jul 3	2. the draft	see this class’ agenda
8	Fri Jul 11	3. the submission	see this class’ agenda
9	Fri Jul 18	4. the presentation	Final Paper & Presentation (due in-class)

This course is split into five main phases:

the setup: students will get set up with all of the tools necessary for writing a research paper in a quantitative discipline
the pitch: students will craft a compelling, challenging research pitch for their term paper
the draft: students will learn about and explore common tools for research paper writing as they prepare the draft for their term paper
the submission: students will get direct feedback for completing their term paper and readying it for publication or submission to a journal
the presentation: students will craft a compelling presentation detailing the research done in process of writing their term paper

Note that the schedule above may shift as we progress through the course. Updates to due dates will be communicated to you by your instructor.

This is a live document. Please refresh this page periodically to check for updates.

Grading¶

Your grade in this course will consist of four components:

Research pitch (15%—due Tue Jun 10)
Term paper submission (25%—due Fri Jul 18)
Research presentation (30%—due Fri Jul 18)
Course participation (30%—assessed overall)

The last component (“course participation”) will be assessed by your instructor based on the following factors:

course attendance
participation in course discussions
regular check-ins & incremental deliverables (as directed by your instructor)
Completion of required weekly 1-hour mentor meetings

Policies¶

The below is a non-exclusive list of policies governing course procedure.

If you have any questions or concerns, it is your responsibility to promptly bring them to the attention of the course instructor.

Extensions & Late Work¶

Due dates for assignments will be communicated to you in advances by your course instructor. If you have questions or confusion about due dates or grading policy, please contact your instructor promptly.

Your instructor is the final word on matters governing this course (where not otherwise superseded by university policy.)

If you need an extension for an assignment, communicate this (in advance) to your instructor. We will make accommodations on a case-by-case basis. We will take into consideration whether your request for an extension was made at the last minute (≤48 hours prior to the due date,) and we reserve the right to deny your request for an extension in accordance with university policy.

Late assignments will be docked a grade for each day that they are late: an A will become an A-; an A- will become a B.

Absences¶

We expect attendance at all in-person sessions of this course.

This is an in-person course, and you are expected to attend all sessions in-person, unless you have requested and been granted accommodations (see below)

In general, absences are excused for illness, religious observation, participation in certain university activities, or there circumstances described in university’s policy. Since we expect the core work of what you do in this class to be performed live in class, with your between-class assignments being about how to improve what you have done in class, participation during course sessions is essential to your performing well and getting the most out of the class. As a result, if you miss more than one class that not covered in one of the explicitly allowed reasons for excused absences, your grade for the class will be reduced.

It is your responsibility to inform the instructor beforehand if you will miss a class. Students must communicate with their instructor regarding expected or unexpected absences.

Note that Howard University policy states:

Students should consult with their instructors and/or TAs when they have missed classes to explain the reasons for their absence and to stay on track in the course. Instructors establish their own policies regarding attendance, and it is the student’s responsibility to know those policies. In certain courses, regular attendance is given special importance. These include foreign language courses and the introductory courses in the Writing Seminars and Expository Writing. Instructors in these courses may lower a student’s grade for unexcused absences.

— Howard University Student Handbook

If you have any questions about this or any other policy, please contact your instructor.

Having emphasized the importance of in-person participation, we recognize that it may occassionally happen that a student has COVID or other very extenuating circumstances (based on instructor approval) that prevent them from attending in-person. For these cases, this is our class’s Zoom link (which will be the same for every class): Zoom Meeting

Accommodations¶

We strive to create a welcoming, effective, and productive learning environment for all students.

Accommodations to course policy or procedure can be made for students on an individual basis, in accordance with university policy.

The American with Disabilities (ADA) Procedures: Howard University is committed to creating an accessible, inclusive, and safe learning environment for all students and providing equal access to students with documented disabilities. Students seeking reasonable accommodations must first register with the Office of Student Services (OSS). There you can engage in a confidential conversation about the process for requesting reasonable accommodations in the classroom and clinical settings, which the Office of Student Services (OSS) determines. Accommodations must be requested each semester. Accommodations are not provided retroactively. If you want to request accommodations, please contact OSS via email at oss.disabilityservices@howard.edu or visit https://howard.edu/disability-services.

If you have any questions about this or any other policy, please contact your instructor.

Mental Health¶

Howard University provides resources to support student wellness. These include resources for anxiety, stress, depression, and other mental health related concerns via the University Counseling Service (202-806-7540). For confidential support related to interpersonal violence, contact the Interpersonal Violence Prevention Program (202-836-1401).

Technology¶

Unlike many other courses you may have taken, this course strongly encourages the use of technology tools, including AI-based writing assistants such as ChatGPT.

You are encouraged to use such tools as directed by your instructors.

However, there may be circumstances where the use of these tools may constitute violations of this course’s or the university’s academic integrity policy.

Please confer with your instructor if you have questions or are unsure about the use of a tool in the completion of your course work.

This course requires the use of a computer to complete course assignments. You will be asked to bring a laptop computer with you to each class session.

If you do not have access to a laptop computer, please contact your instructor for additional guidance.

Communications¶

Course information and announcements will primarily be communicated via Howard University’s Canvas and your HU email. Please check Canvas and your email frequently.

Your instructors will be responsive over email during regular business hours (9 AM ~ 5 PM US/Eastern) during the school week (Monday ~ Friday.) Instructors will use their primary email addresses (listed above) for communication, not necessarily HU email accounts.

We may take up to 48 hours or more to respond to your e-mails; you should not expect a prompt response outside of the times above (e.g., on the weekend). Do note that your instructors have research and instructional obligations that necessitate out-of-town travel and may not be respond promptly to your e-mails.

In addition to Canvas/email, we will use Github for assignment submission and discussion. Please ensure that you regularly check your e-mail and ensure that you can see notifications from Github.

If you need to contact your instructor about assignments, absences, or grade-related matters, please e-mail us well in advance.

Academic Ethics¶

All graded work must be completed in accordance with Howard University’s Academic Code of Student Conduct. In particular, plagiarism in any form is a violation of this code. Examples of plagiarism include, but are not limited to:

buying or borrowing a paper
copying a paper entirely or in part from any source
summarizing a source without adequate citation
using thoughts (including wording) belonging to someone else without citation

It is also a violation of the honor code if the research paper has been used in its entirety in another class. A previous paper of yours may be the basis for further research for the current course, but you must discuss this with and receive written approval from the instructors in advance. The Academic Code specifies consequences for violations. Prior to commencing this course, each student must read the university’s Academic Code, which is available on university’s website.

Digital Etiquette¶

During class-time, please use digital devices such as cellphones, laptops, and tablets for only activities directly involving this class. Other uses (including email, texting, and web browsing) are distracting to your classmates and to the instructors. If you must respond to a phone call, email, or text, please leave the classroom to do so.

COVID-19 Statement for Summer 2025¶

The indoor mask mandate has been lifted on campus for all faculty, staff, students, and visitors, with a notable exception like patient settings. Faculty may continue to require masks for individual classes. In those classes where a face mask is required, students will be directed to leave the classroom if a face mask is not worn properly to cover the nose and mouth. Any student who refuses or fails to comply with a specific classroom requirement to wear a face mask, and any other measures the University advances for the safety and protection of the Howard Community, will constitute a violation of the University’s Student Code of Conduct and could result in sanctions up to and including expulsion from the University.

Statement on Sex and Gender-Based Discrimination, Harassment and Violence¶

Howard University’s Policy Prohibiting Sex and Gender-Based Discrimination, Sexual Misconduct and Retaliation (aka, the Title IX Policy) prohibits discrimination, harassment, and violence based on sex, gender, gender expression, gender identity, sexual orientation, pregnancy, or marital status. With the exception of certain employees designated as confidential, note that all Howard University employees – including all faculty members – are required to report any information they receive regarding known or suspected prohibited conduct under the Title IX Policy to the Title IX Office (TitleIX@howard.edu or 202-806-2550), regardless of how they learn of it. For confidential support and assistance, you may contact the Interpersonal Violence Prevention Program (202-836-1401) or the University Counseling Service (202-806-7540). To learn more about your rights, resources, and options for reporting and/or seeking confidential support services (including additional confidential resources, both on and off campus), visit http://titleix.howard.edu.