statsmodels.regression.recursive_ls.RecursiveLS.fit_constrained¶

RecursiveLS.fit_constrained(constraints, start_params=None, **fit_kwds)¶

Fit the model with some parameters subject to equality constraints.

Parameters:¶

constraints : dict¶

Dictionary of constraints, of the form param_name: fixed_value. See the param_names property for valid parameter names.

start_params : array_like, optional¶

Initial guess of the solution for the loglikelihood maximization. If None, the default is given by Model.start_params.

**fit_kwds : keyword arguments¶

fit_kwds are used in the optimization of the remaining parameters.

Returns:¶

results

Return type:¶

Results instance

Examples

>>> mod = sm.tsa.SARIMAX(endog, order=(1, 0, 1))
>>> res = mod.fit_constrained({'ar.L1': 0.5})