Below I report some of my codes and replications of recent and former models in macroeconomics, relative to inequality, housing and occupational choice.

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

- Solve standard housing macroeconomic models (Sommer & Sullivan AER (2018)) with DC-EGM algorithm (Iskhakov et al. (2017)). Useful note here, code available here.
- Solve the Aiyagari model in 0.04 – 0.14 seconds with Endogenous Grid Method (EGM) (Caroll (2006)). Useful note (by Josep Pijoan-Mas) is available here. Download my code (iterate on marginal utilities or value functions with code here).
- Solve the stochastic growth model as in Barillas & Villaverde (2007) using EGM, code here.
- Discretize income process using Tauchen algorithm in C++: code [here].

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

- Solve Aiyagari in 0.13 seconds with Envelope Condition Method (ECM), many codes available here: HATC project.
- Aiyagari in Continous Time with Jump-Drift Process. Code is available here: aiyagari.m, note: here.
- 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.