ShockTalk is a natural-language interface for building and playing with Dynamic Stochastic General Equilibrium (DSGE) models. It is built on top of a Python library, also named shocktalk, that allows you to create simple, linear DSGE models in plain syntax.

1. Natural language → ShockTalk syntax. You describe the model in plain English (e.g. "Inflation today is driven by expected future inflation, the current output gap, and a cost-push shock."). A language model translates this into ShockTalk's equation syntax.
2. ShockTalk syntax → econpizza YAML. The equations are parsed, validated, and compiled into a econpizza YAML model definition.
3. Simulation. econpizza solves the rational-expectations equilibrium and returns impulse-response paths as a pd.DataFrame.

You can also skip step 1 and write ShockTalk syntax directly — see the example below.

ShockTalk supports log-linearized models — a specific and widely-used approximation technique in macroeconomics.

Most macroeconomic models are inherently nonlinear (e.g. a Cobb-Douglas production function $Y = K^\alpha N^{1-\alpha}$). Log-linearization approximates the model by replacing each variable with its percentage deviation from steady state:

$$\hat{x}_t = \frac{x_t - x^\ast}{x^\ast} \approx \log x_t - \log x^{*}$$

where $x^*$ is the steady-state level of $x$. The resulting equations are linear in the $\hat{x}_t$ terms, which makes the model tractable to solve analytically and simulate efficiently.

Because every variable is expressed as a deviation from its own steady state, the steady state of the transformed system is exactly zero for every variable — by definition. A value of 0.01 in the simulation output means the variable is 1% above its steady-state level; 0 means it is exactly at steady state.

This has a convenient practical consequence: you never need to know or specify the actual steady-state levels ($Y^$, $\Pi^$, etc.). Those levels get divided out in the transformation, so only the parameters governing dynamics — how fast variables return to steady state, how they co-move — need to be supplied. Simulations always start from steady state, meaning all variables begin at zero before any shock is applied.

ShockTalk supports models where every equation is linear in the following terms:

Equations must take the form of a single endogenous variable on the left-hand side, with a linear combination of the terms above on the right-hand side. Coefficients may be arbitrary (nonlinear) expressions in parameters (symbols that are neither variables nor shocks).

A standard three-equation New Keynesian model:

$$ \begin{aligned} y_t &= E_t[y_{t+1}] - \frac{1}{\sigma}(r_t - E_t[\pi_{t+1}]) + \varepsilon^d_t \\ \pi_t &= \beta, E_t[\pi_{t+1}] + \kappa, y_t + \varepsilon^u_t \\ r_t &= \phi_\pi \pi_t + \phi_y y_t \end{aligned} $$

from dsge import DSGE

model = DSGE([
    "y  = F[y] - (1/sigma)*(r - F[pi]) + eps_d",
    "pi = beta*F[pi] + kappa*y + eps_u",
    "r  = phi_pi*pi + phi_y*y",
])

irf = model.simulate(
    parameters={
        "sigma": 1.0,  "beta": 0.99, "kappa": 0.1,
        "phi_pi": 1.5, "phi_y": 0.5,
        "rho_d": 0.8,  "rho_u": 0.8,
    },
    shocks={"e_d": 0.01},  # 1% demand shock in period 0
    T=40,
)

simulate returns a pd.DataFrame with T + 1 rows (periods 0 … T) and one column per endogenous variable, in deviations from steady state:

        eps_d     eps_u        pi         r         y
period
0      0.0100    0.0000    0.0058    0.0116    0.0077
1      0.0080    0.0000    0.0046    0.0093    0.0055
2      0.0064    0.0000    0.0037    0.0074    0.0040
...

• Nonlinear terms in endogenous variables (e.g. y**2, y*pi)
• Leads or lags beyond one period (e.g. F[F[x]], L[L[x]])
• Equations without a unique stable rational-expectations equilibrium (Blanchard-Kahn violations)