arch.univariate.base.ARCHModel¶

class arch.univariate.base.ARCHModel(y: ndarray | DataFrame | Series | None = None, volatility: VolatilityProcess | None = None, distribution: Distribution | None = None, hold_back: int | None = None, rescale: bool | None = None)[source]¶

Abstract base class for mean models in ARCH processes. Specifies the conditional mean process.

All public methods that raise NotImplementedError should be overridden by any subclass. Private methods that raise NotImplementedError are optional to override but recommended where applicable.

Methods

bounds()	Construct bounds for parameters to use in non-linear optimization
compute_param_cov(params[, backcast, robust])	Computes parameter covariances using numerical derivatives.
constraints()	Construct linear constraint arrays for use in non-linear optimization
fit([update_freq, disp, starting_values, ...])	Estimate model parameters
fix(params[, first_obs, last_obs])	Allows an ARCHModelFixedResult to be constructed from fixed parameters.
forecast(params[, horizon, start, align, ...])	Construct forecasts from estimated model
parameter_names()	List of parameters names
resids(params[, y, regressors])	Compute model residuals
simulate(params, nobs[, burn, ...])
starting_values()	Returns starting values for the mean model, often the same as the values returned from fit

Properties

distribution	Set or gets the error distribution
name	The name of the model.
num_params	Number of parameters in the model
volatility	Set or gets the volatility process
y	Returns the dependent variable