I am happy to announce MacroModelling.jl - a package for developing and solving dynamic stochastic general equilibrium (DSGE) models.

The goal of this package is to reduce coding time and speed up model development by providing functions for working with discrete-time DSGE models.

The target audience for the package includes central bankers, regulators, graduate students, and others working in academia with an interest in DSGE modelling.

As of now the package can:

- parse a model written with user friendly syntax (variables are followed by time indices …
`[2], [1], [0], [-1], [-2]`

…, or`[x]`

for shocks) - (tries to) solve the model only knowing the model equations and parameter values -
**no steady state file needed** - calculate
**first, second, and third order**(pruned)**perturbation**solutions (see Villemot (2011), Andreasen et al. (2017) and Levintal (2017)) using (forward or reverse-mode) automatic differentiation (AD) - handle
**occasionally binding constraints** - calculate (generalised) impulse response functions, simulate the model, or do conditional forecasts
- calibrate parameters using (non stochastic) steady state relationships
**match**model**moments**(also for pruned higher order solutions)**estimate**the model on data (Kalman filter using first order perturbation; see Durbin and Koopman (2012)) with gradient based samplers (e.g. NUTS, HMC)**differentiate**(forward AD) the model solution, Kalman filter loglikelihood (reverse-mode AD), model moments, steady state,**with respect to the parameters**

The model has 17 models already implemented, among them Smets and Wouters (2003, 2007), Gerali et al. (2010), QUEST3 (2009), and the New Area-Wide Model (2008).

Writing a model and plotting the impulse response functions is as easy as this:

```
using MacroModelling
import StatsPlots
@model RBC begin
1 / c[0] = (β / c[1]) * (α * exp(z[1]) * k[0]^(α - 1) + (1 - δ))
c[0] + k[0] = (1 - δ) * k[-1] + q[0]
q[0] = exp(z[0]) * k[-1]^α
z[0] = ρ * z[-1] + std_z * eps_z[x]
end;
@parameters RBC begin
std_z = 0.01
ρ = 0.2
δ = 0.02
α = 0.5
β = 0.95
end;
plot_irf(RBC)
```

Note the absence of variable declarations, parameter declarations, or any code related to the non stochastic steady state.

The package provides a range of convenience functions which are explained in the documentation.

I am looking forward to reading your feedback, issues, or pull requests.