AS.440.624.50: Dynamic Stochastic General Equilibrium Modeling
Modeling Macroeconomics | AE Spring 2026
Dynamic Stochastic General Equilibrium Modeling¶
Johns Hopkins University, Advanced Academic Programs 3.0 Credits
Course description¶
This course offers a rigorous introduction to modern macroeconomic theory, building dynamic models from first principles through the intertemporal optimization problems of households and firms. We cover the foundations of consumption theory, asset pricing, investment, and economic growth, culminating in Dynamic Stochastic General Equilibrium (DSGE) frameworks for understanding business cycles and policy effects.
The course emphasizes both analytical and computational methods, with extensive use of mathematical derivations complemented by numerical analysis using Python. Students will master core theoretical results and learn to implement solution techniques ranging from basic optimization to advanced numerical methods. We bridge theory to applications through policy analysis exercises and connections to empirical regularities.
Upon completion, students will have the analytical skills to formulate, solve, and interpret modern macroeconomic models, and the practical skills to implement these models computationally. These theoretical and quantitative skills are highly valued in academia, central banking, consulting, and finance.
Instructor information¶
Instructor | Alan Lujan |
Office hours | By appointment via Zoom scheduler |
Class times | Tuesdays 11:30 am to 2:10 pm |
Location | Room 440 |
Course structure¶
The course combines in-person lectures with hands-on computational work. The first week covers Python programming foundations to establish a common baseline for all students.
Course outline¶
The first week establishes computational foundations with Python programming. The remaining weeks cover five chapters:
Dynamic optimization (Euler equations, Bellman equations)
Perfect foresight and buffer stock models
Consumption under uncertainty
Behavioral and heterogeneous agent extensions
Assignment 1
Portfolio optimization (CARA, CRRA preferences)
Consumption-based asset pricing
Midterm 1
Investment theory (q-theory, Hall-Jorgenson)
Capital market imperfections
Assignment 2 (covers chapters 2-3)
Ramsey-Cass-Koopmans model
Endogenous growth theory
Assignment 3
DSGE model construction and calibration
Business cycle analysis
Final project, Midterm 2
What to expect in this course¶
This course is 15 weeks in length. Each week begins on a Tuesday and ends on the following Monday. Please review the course syllabus thoroughly to learn about specific course outcomes and requirements. Be sure to refer to the Checklist each week, which provides a week-at-a-glance and shows targeted dates for the completion of activities.
The course is organized around five chapters: consumption, asset pricing, investment, growth, and DSGE. Each week includes course discussions, and you will complete three chapter assignments throughout the semester, with the final project serving as the DSGE assessment.
Assessment includes two midterm exams, a final project, and interactive Python assignments completed through DataCamp.
Required materials¶
Textbook¶
Online resources¶
The following free online resources are required reading throughout the course:
Technology requirements¶
Hardware & Software Requirements
Component | Requirement |
|---|---|
Computer | Mac OSX 11+ or PC Windows 7+, with microphone and camera |
Browser | Chrome or Firefox (enable third-party cookies for Canvas integrations) |
Internet | Reliable connection, 5 Mbps or higher |
Software | Python (installation provided in class), Adobe Reader, Microsoft Office 365, Zoom |
Course-specific technology skills¶
This course requires the use of Python for computational exercises. At a minimum, you should be comfortable with:
Navigating and using Canvas
Creating and saving files on your computer
Basic internet research and file management
Sending, receiving, and managing email
Support¶
Evaluation and grading¶
Grading scale¶
Grade | Range |
|---|---|
A | 94% to 100% |
A- | 90% to 93.999% |
B+ | 87% to 89.999% |
B | 83% to 86.999% |
B- | 80% to 82.999% |
C | 70% to 79.999% |
F | 0% to 69.999% |
Grade breakdown¶
Component | Weight |
|---|---|
DataCamp assignments | 10% |
Weekly discussions | 10% |
Chapter assignments (3) | 15% |
Midterm 1 | 20% |
Midterm 2 | 20% |
Final project | 25% |
Assignment descriptions¶
- DataCamp assignments
- Interactive Python exercises completed through DataCamp. These assignments reinforce programming skills and computational methods covered in class. The number of assignments will be announced during the semester.
- Weekly discussions
- Canvas discussion prompts related to the week’s topics. You must post a response to the prompt and reply to at least one colleague’s post.
- Chapter assignments
- Computational exercises completed using Python and Jupyter notebooks. These assignments reinforce the theoretical material through hands-on implementation of models and solution techniques. Collaboration with classmates is permitted, but each student must submit their own work.
- Midterm exams
- Closed-book, in-class assessments consisting of multiple choice questions and free response problems. Questions assess both conceptual understanding and analytical problem-solving skills. Specific time limits and format details will be announced before each exam.
- Final project
- Completed in groups of three students. The project combines computational implementation with a written report and presentation. Groups will take code and concepts learned in the course to develop a creative extension integrating DSGE with one of the other chapters (consumption, asset pricing, investment, or growth). The written component should follow academic paper conventions, including motivation, methodology, results, and interpretation. Groups will deliver a short presentation of their project on the last day of class. A detailed rubric will be provided during the semester.
Assignment submission¶
Assignment feedback¶
The instructor will aim to return assignments within 5-7 days following the due date, depending on assignment length. Lengthy writing assignments may take 10-14 days to grade. Feedback will be available in the Grades area of Canvas.
Learning objectives¶
Program learning outcomes¶
This course contributes to the following Applied Economics program learning outcomes:
PLO1: Analyze advanced economic theory.
PLO4: Construct advanced econometrics and other quantitative models to estimate, test, and forecast economic relationships.
Course learning outcomes¶
Upon completion of this course, students will be able to:
Solve dynamic optimization problems using Euler equations, Bellman equations, and Python computational tools.
Compare consumption models under rational, behavioral, and uncertainty assumptions using mathematical derivations and empirical analysis.
Construct portfolio optimization and asset pricing models including CARA, CRRA, and consumption-based approaches.
Evaluate investment decisions using q-theory, Hall-Jorgenson, and capital market imperfection frameworks.
Analyze growth dynamics through Ramsey-Cass-Koopmans, endogenous growth, and computational implementations.
Build and calibrate DSGE models integrating household, firm, and government sectors for business cycle analysis.
Course policies¶
Course amendments¶
Changes to the course will be posted in the Announcements section of Canvas or announced in class. Please check announcements every time you log into Canvas.
Email communication¶
Late policy¶
Extra credit¶
There are no extra credit opportunities in this course.
Participation and attendance¶
Education requires the active involvement of students in the learning process. Students are expected to attend all classes and actively engage in all learning assignments and opportunities. For this in-person course, attendance should be treated as mandatory.
AAP’s Attendance Policy can be found in the course catalog.
Artificial intelligence policy¶
University policies¶
This course adheres to all University policies described in the academic catalog.
Academic conduct
All JHU students assume an obligation to conduct themselves in a manner appropriate to the Johns Hopkins University’s mission as an institution of higher education and with accepted standards of ethical and professional conduct. Students must demonstrate personal integrity and honesty at all times in completing classroom assignments and examinations, in carrying out their fieldwork or other applied learning activities, and in their interactions with others.
Students and faculty in Advanced Academic Programs are required to adhere to the academic integrity guidelines and process laid out in the Graduate Academic Misconduct Policy.
Student work may be submitted to an online plagiarism detection tool at the discretion of the course instructor.
Ethics and plagiarism
The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include cheating on exams, plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. Report any violations you witness to the instructor.
Read and adhere to JHU’s Notice on Plagiarism.
Copyright policy
All course materials are the property of JHU and are to be used for the student’s individual academic purpose only. Any dissemination, copying, reproducing, modification, displaying, or transmitting of any course material content for any other purpose is prohibited and may be cause for disciplinary action under the JHU Copyright Compliance Policy.
Recordings, course materials, and lecture notes may not be exchanged or distributed for commercial purposes, for compensation, or for any purpose other than use by students enrolled in the class.
Student conduct code
The fundamental purpose of the Johns Hopkins University’s regulation of student conduct is to promote and protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University.
For a full description, visit the Student Conduct Code.
Accommodations and accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including psychological conditions, medical conditions, and temporary disabilities) can request accommodations by providing an Accommodation Letter issued by Student Disability Services (SDS).
Please request accommodations as early as possible. Contact AAP Student Disability Services at AAPDisability@jh
Dropping the course
You are responsible for understanding the university’s policies and procedures regarding withdrawing from courses found in the current catalog. Be aware of the current deadlines according to the Academic Calendar.
Getting help
You have a variety of methods to get help on Canvas. Please consult the resource listed in the “Help” link for important information. If you encounter technical difficulty in completing or submitting any online assessment, please immediately contact the designated help desk listed on the AAP online support page. Also, contact your instructor at the email address listed in the syllabus.
Title IX and mandatory reporting
As an instructor, I have mandatory reporting responsibilities related to my role as a Responsible Employee under the Sexual Misconduct Policy and Procedures (which prohibits sexual harassment, sexual assault, relationship violence, and stalking), as well as the General Anti-Harassment Policy.
I will seek to keep information you share private to the greatest extent possible. However, I am required to share information regarding sexual misconduct, as well as protected status based harassment and discrimination, with the Office of Institutional Equity (OIE).
For a list of individuals who can speak with you confidentially, see Appendix B of the JHU Sexual Misconduct Policies and Laws.
Diversity statement
Johns Hopkins is a community committed to sharing values of diversity and inclusion in order to achieve and sustain excellence. We firmly believe that we can best promote excellence by recruiting and retaining a diverse group of students, faculty, and staff and by creating a climate of respect that is supportive of their success.
For more information, visit the Diversity at JHU website.
Course evaluation¶
Please remember to complete an online course evaluation survey for this course. These evaluations are important for ongoing efforts to improve instructional quality. Results are kept anonymous; your instructor will only receive aggregated data and comments. An email with a link to the evaluation form will be sent near the end of the semester.