**[EGM]**Endogenous Grid Method: Caroll (2006)**[DC-EGM]**algorithm to solve discrete-continuous choice models using EGM: Iskhakov et al. (2017).*[G2-EGM]*Multi-dimensional DC-EGM algorithm: Druedhal and Jørgensen (2017).*[N-EGM]*Nested Engodenous Grid Method :: Hintermaier and Koeniger (2012), Druedhal (2019)

**[EXPECTATION 1]**Useful tool to approximate expectations over normally distributed shocks: Gauss-Hermite + Monomial Rule.**[EXPECTATION 2]**Computing expectations of value functions using polynomials: Judd et al. (2017)**[SIMULATION]**Non-stochastic simulation routine: Young (2010)**[COLOCATION]**Collocation Method: Collocation**[ECM]**Envelop Condition Method (ECM): Maliar (2013)**[SMM]**Estimating HA models :: Sobol sequence**[VAR, AR(1)]**Discretizing VAR: Farmer et Toda (2013)

```
int C++ // (for discrete time)
```

**[HOUSING]**Solve standard housing macroeconomic models with DC-EGM, Sommer & Sullivan AER (2018). Useful note here, code available*upon-request*.**[AIYAGARI]**Solve the Aiyagari model in 0.04 – 0.14 seconds with EGM + Young method (2010). Useful note (by Josep Pijoan-Mas) is available here. Download my code (iterate on marginal utilities or value functions with code available*upon-request*).**[SGM]**Solve the stochastic growth model using EGM, Barillas & Villaverde (2007), code here.**[ENTREPRENEURSHIP]**Solve Cagetti & DeNardi (2006) in 3s with DC-EGM, code available*upon-request*.

```
MatLab % (for continuous time)
```

**[AIYAGARI]**Solve Aiyagari in 0.13 seconds with Envelope Condition Method (ECM), many codes available here: HATC project.**[AIYAGARI]**Aiyagari in Continous Time with Jump-Drift Process. Code is available*upon-request*, note: here.**[HANK]**Heterogenous Agent New Keynesian (HANK) (Kaplan et al. (2018)) model and the code available here: (not yet available), note: here.

Comparison of performance and accuracy of EGM, DC-EGM and VFI methods on occupational choice and entrepreneurship models à la Cagetti & De Nardi (2006). The presence of discrete choice (occupational choice) makes EGM inaccurate. DC-EGM encompasses generated kinks very well, while being substantially faster than standard VFI.

Method | Speed (in s) | % Entrepreneurs | K/Y |
---|---|---|---|

EGM | 0.8s | 8.4 | 2.6 |

DC-EGM | 1.2s | 8.8 | 2.6 |

VFI | 3s | 8.8 | 2.6 |

- Jean-Pierre’s Moreau Homepage: useful codes and routines in C++ and Fortran.
- John Starchulski and Tom Sargent’s QuantEcon: useful codes in Julia / Python.
- Paul Bourke: useful tool for interpolations.
- John Burkardt: useful tool for many languages.
- Maliar et Maliar’s Book: all.