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Macroeconometrics

Bayesian Estimation of RANK and HANK Business Cycle Models

Advanced Bayesian methods for estimating structural business cycle models.

17.5h (5 days)

€1,470

Face-to-face

English

Next edition: July 5-9, 2027

Early bird deadline: April 15, 2027

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Macroeconometrics

Bayesian Estimation of RANK and HANK Business Cycle Models

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Who is this Course for?

Bayesian Estimation of RANK and HANK Business Cycle Models

Course overview

The objective of the course is to teach students how to use state-of-the-art Bayesian methods to estimate and analyze structural business cycle models. The course covers the foundations of Bayesian inference, model solution methods, MCMC posterior sampling methods, and the key components of Bayesian analysis and model evaluation.

Students will (i) learn how to formulate, solve, and estimate both representative (RANK) and heterogeneous (HANK) New Keynesian models, (ii) learn how to construct posterior probability distributions for model outputs, (iii) learn how to evaluate the relative role of prior information, the data, and model structure for model outputs, and (iv) gain an understanding of the challenges and opportunities involved with solving and estimating HANK models relative to RANK models.

Faculty

Who is this course for?

This course is designed for:

Researchers and practitioners working at central banks as well as other private and public institutions
Master’s and PhD students who want to extend their knowledge in macroeconometrics and learn more about frontier research topics

Learning outcomes

Upon completion of this course, you will have:

Learned how to formulate, solve and estimate both representative (RANK) and heterogeneous (HANK) New Keynesian models
Learned how to construct posterior probability distributions for model outputs
Learned how to evaluate the relative role of prior information, the data and model structure for model outputs
Gained an understanding of the challenges and opportunities involved with solving and estimating HANK models relative to RANK models

Key topics for Bayesian Estimation of RANK and HANK Business Cycle Models course

This course will cover the following:

Introduction to Bayesian Inference and Representative Agent New Keyensian (RANK) models

Introduction to Bayesian analysis
Macro models as data generating processes
Formulating and solving a representative agent New Keynesian business cycle model

State Space Models and Likelihood Based Estimation

State space models
The Kalman filter
Likelihood based estimation
Numerical maximization

Bayesian Estimation of Linearized RANK Models

Bayesian computation and sampling from a target distribution
Formulating priors for a macroeconomic model
Simulating from the posterior distribution of a business cycle model

Bayesian Analysis of Structural Model

Efficient and robust posterior simulation
Constructing probability intervals of model outputs
Prior predictive analysis
Bayesian model averaging

Bayesian Estimation of Heterogeneous Agent New Keynesian (HANK) Models

Specifying, solving and estimating heterogeneous agent models. What are the key differences and challenges compared to RANK models?
Solving HANK models
Evaluating the likelihood function for a HANK model
Formulating priors and sampling from the posterior

List of References

Below is a list of textbooks that may be useful before, during, and after the course. References are split by lectures.

Lecture 1

Berger, J. O. (2013). Statistical Decision Theory and Bayesian Analysis. Springer Science & Business Media.
De Finetti, B. (2008). Philosophical Lectures on Probability, collected, edited, and annotated by A. Mura. Springer Science & Business Media.
del Negro, M., and Schorfheide, F. Bayesian Macroeconometrics. In The Oxford Handbook of Bayesian Econometrics.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. CRC Press.
Herbst, E. P., and Schorfheide, F. (2015). Bayesian Estimation of DSGE Models. Princeton University Press.
Robert, C. (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation. Springer Science & Business Media.Robert, C., and Casella, G. (2013). Monte Carlo Statistical Methods. Springer Science & Business Media.
Söderlind, P. (1999). Solution and Estimation of RE Macromodels with Optimal Policy. European Economic Review, 43(4–6), 813–823.

Lecture 2

Hamilton, J. D. (1994). Time Series Analysis, Chapter 5 (State-Space Models and the Kalman Filter). Princeton University Press.
Goffe, W. L., Ferrier, G. D., and Rogers, J. (1994). Global Optimization of Statistical Functions with Simulated Annealing. Journal of Econometrics, 60(1-2), 65–99.
Durbin, J., and Koopman, S. J. (2002). A Simple and Efficient Simulation Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603–616.
Stock, J. H., and Watson, M. W. (2007). Why Has US Inflation Become Harder to Forecast? Journal of Money, Credit and Banking, 39(s1), 3–33.

Lecture 3

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21(6), 1087–1092.
Hastings, W. K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57(1), 97–109.
Geman, S., and Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741.
Chib, S., and Greenberg, E. (1995). Understanding the Metropolis–Hastings Algorithm. The American Statistician, 49(4), 327–335.
An, S., and Schorfheide, F. (2007). Bayesian Analysis of DSGE Models. Econometric Reviews, 26(2–4), 113–172.

Lecture 4

Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2), 461–464.
Geweke, J. (1999). Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication. Econometric Reviews, 18(1), 1–73.
Kass, R. E., and Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773–795.
Gerard, H., and Nimark, K. (2008). Combining Multivariate Density Forecasts Using Predictive Criteria. Reserve Bank of Australia Research Discussion Paper RDP 2008-02.

Lecture 5

Auclert, A., Bardóczy, B., Rognlie, M., and Straub, L. (2021). Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models. Econometrica, 89(5), 2375–2408.
Krusell, P., and Smith, A. A. Jr. (1998). Income and Wealth Heterogeneity in the Macroeconomy. Journal of Political Economy, 106(5), 867–896.
McKay, A., Nakamura, E., and Steinsson, J. (2016). The Power of Forward Guidance Revisited. American Economic Review, 106(10), 3133–3158.
Kaplan, G., Moll, B., and Violante, G. L. (2018). Monetary Policy According to HANK. American Economic Review, 108(3), 697–743.
Reiter, M. (2009). Solving Heterogeneous-Agent Models by Projection and Perturbation. Journal of Economic Dynamics and Control, 33(3), 649–665.
Bayer, C., Born, B., and Luetticke, R. (2024). Shocks, Frictions, and Inequality in US Business Cycles. American Economic Review, 114(5), 1211–1247.
Smets, F., and Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.
Kase, H., Melosi, L., and Rottner, M. (2024). Estimating Nonlinear Heterogeneous Agent Models with Neural Networks. BIS Working Paper No. 1241.
Bilbiie, F. O., Primiceri, G. E., and Tambalotti, A. (2023). Inequality and Business Cycles. NBER Working Paper No. 31729.

Software / Hardware

MATLAB will be used during the course. Participants will receive a free, time-limited MATLAB license before the program starts. Please install MATLAB on your computer before the course begins for use in practical sessions
Participants must bring their own laptop to participate in the practical sessions

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

All BSE Summer School applicants must meet the entrance requirements.

Next edition: July 5-9, 2027

Early Bird deadline: April 15, 2027

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Bayesian Estimation of RANK and HANK Business Cycle Models

Basic knowledge of time series econometrics

Schedule

Here is your schedule for this edition of BSE Macroeconometrics Bayesian Estimation of RANK and HANK Business Cycle Models Summer School course.

Time

5

mon

6

tue

7

wed

8

thu

9

fri

09:00-11:00

Lecture

14:30-16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Course

Bayesian Estimation of RANK and HANK Business Cycle Models

Advanced Bayesian Macroeconometrics

Forecasting with Machine Learning

Introductory Bayesian Macroeconometrics

Introduction to Nowcasting and Forecasting

Structural Macroeconometrics: Foundations

Structural Macroeconometrics: Advanced Topics

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

€1,470

Reduced Fee*

€840

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

Multiple discounts are available.

For more information on discounts for participants funding their own course, please click here.

For more information on discounts for companies and institutions enrolling their employees, please click here.

Full discount conditions are available here.

FAQ

Here are some commonly asked questions by participants. Any further queries, please contact our Admissions Team.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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