mc_grid

simulate.mc_grid(
    Ns=(16, 36, 64),
    T0s=(20, 50),
    T1=10,
    rhos=(-0.8, -0.3, -0.1, 0.0, 0.1, 0.3, 0.8),
    sims_per=1000,
    K=1,
    beta=(1.0),
    sigma2=0.1,
    treated=(0, 1, 2, 3),
    m_iter=6000,
    burn=1000,
    step_rho=0.05,
    n_jobs=1,
    seed=None,
    backend='auto',
    progress=False,
)

Run the paper’s Monte Carlo grid (Tables 1-2 design).

Defaults reproduce the replication package’s full study: rook lattices of N in {16, 36, 64} units, pre-periods T0 in {20, 50}, spillover intensities rho in {-0.8, ..., 0.8}, 1000 replications per cell, one N(0, 1) covariate with coefficient 1, error variance 0.1, and the paper’s planted alpha. Reduce sims_per / the grids for quicker runs.

Parameters

Name	Type	Description	Default
Ns	tuple of int	Numbers of control units; each must be a perfect square.	`(16, 36, 64)`
T0s	tuple of int	Pre-treatment lengths.	`(20, 50)`
T1	int	Post-treatment length.	`10`
rhos	tuple of float	True spillover intensities.	`(-0.8, -0.3, -0.1, 0.0, 0.1, 0.3, 0.8)`
sims_per	int	Replications per scenario.	`1000`
K	DGP settings	See :func:`~scspill.simulate.dgp.scspill_sim_dgp`.	`1`
beta	DGP settings	See :func:`~scspill.simulate.dgp.scspill_sim_dgp`.	`1`
sigma2	DGP settings	See :func:`~scspill.simulate.dgp.scspill_sim_dgp`.	`1`
treated	DGP settings	See :func:`~scspill.simulate.dgp.scspill_sim_dgp`.	`1`
m_iter	MCMC budget	Sampler settings per replication (the paper uses 6000/1000/0.05).	`6000`
burn	MCMC budget	Sampler settings per replication (the paper uses 6000/1000/0.05).	`6000`
step_rho	MCMC budget	Sampler settings per replication (the paper uses 6000/1000/0.05).	`6000`
n_jobs	int	Parallel workers per scenario (see :func:`~scspill.simulate.runner.run_many_sim`).	`1`
seed	int	Master seed; per-scenario seed lists are spawned deterministically.	`None`
backend	('auto', 'numpy', 'numba')	Sampler kernel backend.	`"auto"`
progress	bool	Print one line per completed scenario.	`False`

Returns

Name	Type	Description
	pd.DataFrame	Tidy results in the frozen R schema: `N`, `T0`, `T1`, `rho`, `method`, `bias_point`, `rmse_point`, `cover95_point`.