Numerical Methods (2014)

Root finding and how to solve the Mortensen-Pissarides model and various disputes within the discipline
- Bisection, Brent's method
- Quasi-Newton (and what's quasi- about it?)
- Derivative-free methods
- Representing AR process as Markov chains
- Castaneda et al, and using Markov chains to understand wealth
Local, perturbation methods and how to solve mostly-smooth neoclassical growth models
- Order-n local approximation
- Perturbation around a non-stochastic steady state
- Computing the approximation by undetermined coefficients
- Approximating derivatives by finite differences
Tests for accuracy
- Ad hoc tests for accuracy
- Euler Equation errors
- Den Haan Marcet test statistic based on simulations
- Improving the accuracy by non-linear change of variables
Estimating models by using models
- What is the "goal" of calibration
- How to treat the data
- Why simulate
- Which moments to match
- Making outcomes continuous in parameters
Global methods for solving models with heterogenity
- Value function iteration and solving on the full state space
- Functional approximation
- Integration
- Endogenous grid points and generalized endogenous grid points
- Parameterized expectations and simulations to solve the model
Some thoughts on microdata sources and how to use them

Some introduction to the lingo

programming essentials slides

fortran and matlab templates (code)

c template (code)

Root finding and markov chain approximation

solving equilibrium models of unemployment with search and matching

Final project due Dec. 19

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