Wouter J. den Haan - CFM-LSE Numerical Summer School
2023
The first Numerical Summer School was held in 2009 in Amsterdam. More than a thousand students have now taken one of the two courses. (See previous participants' evaluations below.)
When?
- The essentials: July 17-21
- One week break.
- Advanced: July 31 - August 4
Key features
- AS ALWAYS: 3-hour intensive computer lab sessions to really learn the material.
- Either in-person in London or online.
- NEW: Prerecorded lectures so students can be better prepared for lab sessions.
- NEW: Guided Q&As to accompany pre-recorded lectures.
- NEW: Adjusted timing to accommodate different time zones.
Daily schedule and how the course works for different time zones: click HERE
Student Fee: £475 per week
Regular Fee: £625 per week
Those with a Ukrainian passport: Free
Email:numerical.summerschool@protonmail.com
Registration:
- Necessary background!!! To be admitted to these courses, you MUST have taken graduate-level university courses in macroeconomics covering dynamic programming. In addition, you should have some coding experience, ideally with MATLAB. If you cannot document that you have taken graduate-level courses in macroeconomics, then your application will be rejected.
- Click HERE to register. After you have filled out the application, we will check whether you satisfy the necessary prerequisites and if so send you an invoice.
- Terms and Conditions.
- Tips on international money transfers.
- There is no strict registration deadline except that we typically reach capacity at some point (usually beginning of July).
London accommodation tips:
Hard/Software requirements:
- You will need access to Zoom to participate in the lectures.
- And access to Matlab for the lab session; no toolboxes are required.
- When you sign up for the in-person lab sessions, then you need to bring your own laptop with Matlab installed.
The two courses:
-
The essentials; Solving and estimating DSGE models
This graduate-level course teaches the key building blocks of numerical analysis such as function approximation and numerical integration. The course shows how these techniques are used in perturbation and projection methods to accurately solve nonlinear dynamic stochastic models. Relevant theoretical aspects such as the Blanchard-Kahn conditions and the possibility of sun spots solutions are also covered. The course also teaches the tools to estimate such models (Kalman filter, Bayesian estimation, MCMC). Students are taught how to use Dynare, but also how to write Matlab programs to solve a variety of models with other techniques. In addition to teaching techniques such as projection methods (policy function iteration) and parameterized expectations, the course also focuses on practical problems that researchers run into when using these techniques. - Advanced tools This graduate-level course teaches state-of-the art techniques to solve and analyse advanced models. We will cover models with heterogeneous agents, continuous time models, and models with occasionally binding constraints, specifically models in which the economy can be at the zero lower bound for the policy interest rate. In addition to teaching techniques, the course also focuses on practical problems that researchers run into when using these methods. These courses are aimed at graduate students and academics.
Detailed course content 2022 :
There may be some changes in 2023
Instructors:
Evaluations of previous summer courses:
- Part I (August 2010): 4.95 (out of 5)
- Part I (August 2010): 4.89 (out of 5)
- Part II (August 2010): 4.89 (out of 5)
- Part I (August 2011) recommendation rate: 100%
- Part II (August 2011) recommendation rate: 100%
- Part I (August 2014) recommendation rate: 100%
- Part II (August 2014) recommendation rate: 100%
- Part I (August 2015) recommendation rate: 100%
- Part II (August 2015) recommendation rate: 100%
- Part I (August 2016) recommendation rate: 98%
- Part II (August 2016) recommendation rate: 98%
- Part I (August 2017) recommendation rate: 100%
- Part II (August 2017) recommendation rate: 100%
- Part I (July 2021) recommendation score: 9.40 (out of 10)
- Part II (July 2021) recommendation score: 9.08 (out of 10)
- Part I (July 2022) recommendation score: 4.75 (out of 5)
- Part II (July 2022) recommendation score: 4.77 (out of 5)
- Detailed comments can be found here, here, and here.
Key elements of each course: