Wouter J. den Haan - LSE Macroeconomics Summer Courses
Macroeconomics Summer Courses - August 2016
#2 Advanced Tools: Solving and estimating advanced macroeconomic models
August 24-28
Instructors:
- Wouter J. den Haan, main instructor
- Pontus Rendahl, two days
Prerequisites:
- knowledge about dynamic models, e.g., know what an Euler and a Bellman equation are
- Knowledge on programming with Matlab
- Knowledge on solving dynamic models with a representative agent like Dynare and value function iteration
- Knowledge of the Kalman filter
Course outline 2016:
We are planning to change the content of the program somewhat relative to what was taught in previous years. Below you find our plans, but there may be some changes relative to what is proposed here.
Monday and Tuesday - Solving and simulating models with heterogeneous agents
- Overview: We will look at popular algorithms used to solve models with heterogeneous agents and with aggregate risk. We will go through their implementation and discuss their strengths and weaknesses. The pioneering algorithm of Krusell and Smith (1998) is often reliable, but it is also quite slow and we will discuss improvements. In particular, we will discuss how to efficiently compute a stochastic simulation which avoids sampling uncertainty, and we will discuss alternative techniques which avoids simulation all together. We will discuss ways to impose market clearing, which in some applications is a non-trivial and important issue. we will teach you certain "tricks" to deal with this. We will also discuss how to deal with portfolio problems, asset pricing, and the introduction of money in these types of models. Lastly, we will discuss how to exploit linearization techniques when issues like the zero lower bound are present.
- Topics:
- Simulation and distributions
- Krusell & Smith algorithm to solve models with heterogeneous agents and aggregate uncertainty
- Avoiding sampling uncertainty
- Xpa algorithm to solve models with heterogeneous agents aggregate uncertainty
- Obtaining the ergodic distribution quickly (without simulating) as the Eigenvector of the matrix in the transition equation.
- Applications & exercises - Monday and Tuesday: TBA, (last year we asked students to solve a simple equilibrium models with rational heterogenous agents and students were asked to compare properties of this more complex model with the representative agent equivalent).
Wednesday and Thursday - Models with occassionally binding constraints and continuous-time models
- Overview: Most macroeconomic analysis takes place in discrete time. But some problems are better dealt with in continuous time. These two days we focus on continuous-time models and explore numerical algorithms to solve them. During these days, we will discuss how to exploit linearization techniques in a smart way and deal with occasionally binding constraints such as the zero-lower-bound in monetary models.
- Topics:
- Continuous-time models
- Algorithms to solve models in continuous time
- Models with occassionally binding constraints.
- Zero-lower-bound models and algorithms
- Applications & exercises - Tuesday: TBA, (but probably an excersize where you are asked to use linearization tools to solve models in which the zero lower bound is binding and a continuous-time problem)
Friday - Advanced empirical techniques
- Overview: A big part of this day is devoted to VARs with time-varying coefficients. But we will also spend some time on useful practical elements such as implementing sign restrictions and getting standard errors for business cycle statistics
- Topics:
- Calculating standard errors for business cycle statistics
- Reduced-form VARs
- Structural VARs
- Identification (e.g., Cholesky decomposition)
- Sign restrictions
- VARs with time-varying coefficients
- Applications & exercises: The exercise will ask you to estimate a time-series model for GDP with time-varying coefficients
