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.

Parameters:¶

y: ndarray | DataFrame | Series | None = None¶

The dependent variable.

volatility: VolatilityProcess | None = None¶

Volatility process to use in the model. If not specified, a constant variance is used.

distribution: Distribution | None = None¶

Distribution of the standardized residuals. If not specified, a standard normal is used.

hold_back: int | None = None¶

Number of observations at the start of the sample to exclude when estimating model parameters. Used when comparing models with different lag lengths to estimate on the common sample. Must not be negative.

rescale: bool | None = None¶

Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation. If True, then y is rescaled and the new scale is reported in the estimation results. If None, the data is not rescaled and a warning is issued when its scale is likely to produce convergence issues.

Raises:¶

• TypeError – If volatility is not a VolatilityProcess, distribution is not a Distribution, hold_back is not an integer or rescale is not a bool.

• ValueError – If hold_back is negative or y contains NaN or inf.

Methods

append(y[, x])	Append observations to the model in-place.
bounds()	Construct bounds for parameters to use in non-linear optimization.
compute_param_cov(params[, backcast, robust])	Compute 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])	Construct an ARCHModelFixedResult from fixed parameters.
forecast(params[, horizon, start, align, ...])	Construct forecasts from estimated model.
parameter_names()	Return the names of the parameters of the mean model.
resids(params[, y, regressors])	Compute model residuals.
simulate(params, nobs[, burn, ...])	Simulate data from the model.
starting_values()	Return starting values for the mean model.

Properties

distribution	Get or set the error distribution.
name	The name of the model.
num_params	Number of parameters in the model.
volatility	Get or set the volatility process.
y	The dependent variable.