Seminar

The winter school is designed for PhD students and professionals seeking training in methods for solving and estimating heterogeneous agent business cycle models, particularly HANK models. While the focus is on technical training, the program also covers some economic aspects of heterogeneous agent macro models.

The school runs for two weeks (eight days). Each day consists of four hours of lectures and three hours of small-group training sessions (with a maximum of ten participants per group). The first week is online, and the second week takes place in person at the DIW Berlin.

The school provides all the necessary training to become a proficient user of the BASEforHANK package (https://github.com/BASEforHANK/BASEtoolbox.jl), which allows HANK models to be solved and estimated efficiently in a Dynare-like environment.

The programming language used at the school is Julia; therefore the school also provides an introduction to Julia as a programming language. You can apply to attend either week separately, but a solid knowledge of the material covered in week one is a prerequisite for week two.

Week 1: Basic methods for HANK (online)
Dec. 8 Basics of dynamic programming, value and policy function iteration
Dec. 9 Endogenous grid method (EGM), Markov chains, distributional EGM
Dec. 10 Stationary distributions, stationary equilibria
Dec. 11 Solving DSGE models by perturbation, Kalman filter
Dec. 12 Estimation of DSGE models

Week 2: Advanced methods for HANK (on-site)
Dec. 15 Overview of techniques to solve HANK models
Dec. 16 State-space reduction techniques, aggregate uncertainty
Dec. 17 Estimating HANK models, applications

The BASEforHANK Winter School school runs for a week and a half (eight days). Each day consists of four hours of lectures (9 a.m. - 1 p.m., Berlin time) and three hours of small-group training sessions (with a maximum of ten participants per group, 2 p.m. - 5p.m. Berlin time).

The first week of the course begins with an overview of efficient methods for solving single-agent dynamic programming problems, as described in Carroll (2006) and Hintermaier and Koeniger (2010). The dynamic household problems studied here generate distributions of income and wealth over time, described by a Kolmogorov forward equation. Next, we will study methods to efficiently solve for stationary distributions in this setup, which will allow us to describe and compute Aiyagari (1994) and Huggett (1993)-type stationary equilibria. We will then discuss solution techniques (in particular Klein, 2000; Schmitt-Grohé and Uribe, 2004) for solving dynamic stochastic general equilibrium models based on their state-space form and perturbation techniques. Finally, we will discuss Bayesian estimation of DSGE models in state-space form based on filtering techniques. The application example will be a representative agent New Keynesian model of the type studied, for example, by Christiano et al. (2005) and Smets and Wouters (2007).

The second week focuses on HANK models. We will begin with an overview of solution techniques for HANK models (see e.g., Ahn et al., 2018; Auclert et al., 2021; Krusell and Smith, 1998; Reiter, 2009). We will then focus on state-space techniques based on model reduction (Bayer et al., 2024; Bayer and Luetticke, 2020). This will include techniques for solving HANK models at higher order (Bayer et al., 2025), as well as methods that allow us to study the effects of aggregate uncertainty in environments with ambiguity-averse heterogeneous agents (Ilut et al., 2025). Finally, we will introduce the BASEforHANK toolbox, the estimation techniques developed in Bayer et al. (2024), and their application to studying business cycles, monetary and fiscal policy.

Ahn, SeHyoun, Greg Kaplan, Benjamin Moll, Thomas Winberry, and Christian Wolf (2018). “When inequality matters for macro and macro matters for inequality”. NBER Macroeconomics Annual 32 (1), 1–75.

Aiyagari, S Rao (1994). “Uninsured idiosyncratic risk and aggregate saving”. The Quarterly Journal of Economics 109 (3), 659–684.

Auclert, Adrien, Bence Bardóczy, Matthew Rognlie, and Ludwig Straub (2021). “Using the sequence-space Jacobian to solve and estimate heterogeneous-agent models”. Econometrica : journal of the Econometric Society 89 (5), 2375–2408.

Bayer, Christian, Benjamin Born, and Ralph Luetticke (2024). “Shocks, frictions, and inequality in US business cycles”. American Economic Review 114 (5), 1211–1247.

Bayer, Christian and Ralph Luetticke (2020). “Solving discrete time heterogeneous agent models with aggregate risk and many idiosyncratic states by perturbation”. Quantitative Economics 11 (4), 1253–1288.

Bayer, Christian, Ralph Luetticke, Maximilian Weiss, and Yannik Winkelmann (2025). “An endogenous gridpoint method for distributional dynamics”. CEPR Discussion Paper 19067.

Carroll, Christopher D. (2006). “The method of endogenous gridpoints for solving dynamic stochastic optimization problems”. Economics Letters 91 (3), 312–320.

Christiano, Lawrence J, Martin Eichenbaum, and Charles L Evans (2005). “Nominal rigidities and the dynamic effects of a shock to monetary policy”. Journal of Political Economy 113 (1), 1–45.

Hintermaier, Thomas and Winfried Koeniger (2010). “The method of endogenous gridpoints with occasionally binding constraints among endogenous variables”. Journal of Economic Dynamics and Control 34 (10), 2074–2088.

Huggett, Mark (1993). “The risk-free rate in heterogeneous-agent incomplete-insurance economies”. Journal of Economic Dynamics and Control 17 (5-6), 953–969.

Ilut, Cosmin L, Ralph Luetticke, and Martin Schneider (2025). “Hank’s response to aggregate uncertainty in an estimated business cycle model”. NBER Working Paper 33331.

Klein, Paul (2000). “Using the generalized Schur form to solve a multivariate linear rational expectations model”. Journal of Economic Dynamics and Control 24 (10), 1405–1423.

Krusell, Per and Anthony A. Smith Jr. (1998). “Income and wealth heterogeneity in the macroeconomy”. Journal of Political Economy 106 (5), 867–896.

Reiter, Michael (2009). “Solving heterogeneous-agent models by projection and perturbation”. Journal of Economic Dynamics and Control 33 (3), 649–665.

Schmitt-Grohé, Stephanie and Martin Uribe (2004). “Solving dynamic general equilibrium models using a second-order approximation to the policy function”. Journal of Economic Dynamics and Control 28 (4), 755–775.

Smets, Frank and Rafael Wouters (2007). “Shocks and frictions in US business cycles: a Bayesian DSGE approach”. American Economic Review 97 (3), 586–606.

Regular participation fees 

The participation fees for the winter school are
• € 800 for week 1,
• € 1,200 for week 2, and
• a discounted rate of € 1,600 if both weeks are booked together.

Reduced participation fees
PhD students and junior researchers from higher education institutions and research organisations can apply for an additional 37.5% discount, bringing the respective rates down to
• € 500 for week 1,
• € 750 for week 2, and
• € 1,200 for both weeks.

Lunches and coffee will be provided in week two, but participants need to book their own accommodation. We do not cover any travel or accommodation expenses occurring for participants of the on-site module at DIW Berlin.

The call for applications has been closed. It's not possible to apply at this stage. Thank you for your understanding.

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