EMINN implementation of Krusell-Smith model with neural network solutions.

Reconstructs and updates Coibion & Gorodnichenko (2012 JPE, “What Can Survey Forecasts Tell Us about Information Rigidities?”) oil and technology shock series using FRED data and the original VAR code, providing ready-to-use macro shocks.

Replicates and extends Furlanetto et al. (2023, “Estimating Hysteresis Effects”) by estimating their original VAR model on updated pre-COVID data to produce an updated set of macro shock series spanning through and after the COVID period.

End-to-End Python replication of Camara & Aublin's (2025) monetary spillover analysis methodology. Implements rotational-angle decomposition, Bayesian VAR with Normal-Wishart priors, sign restrictions for shock identification, and a full robustness suite for international macroeconomic analysis.

A simple procedure to calibrate, solve, simulate and evaluate a RBC baseline model on Matlab.

End-to-End Python implementation of Dávila-Fernández & Sordi's (2025) methodology for FX-constrained growth modeling (in emerging markets). Features Bayesian state-space estimation via Gibbs sampling with FFBS algorithm, heterogeneous agent simulation (fundamentalists/chartists), and nonlinear dynamics analysis.