GarchModel Class

Represents a "Generalized Autoregressive Conditional Heteroskedasticity" (GARCH) model.

Definition

Namespace: Numerics.NET.Statistics.TimeSeriesAnalysis
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.3
C#
public class GarchModel : TimeSeriesModel<double>
Inheritance
Object  →  Model  →  TimeSeriesModel<Double>  →  GarchModel

Remarks

Use the GarchModel class to model a time series where the expected value of the error terms may vary over time. GARCH models

Constructors

GarchModel(Int32) Constructs a new ARCH model of the specified order.
GarchModel(Int32, Int32) Constructs a new GARCH model of the specified order.
GarchModel(Vector<Double>, Int32) Constructs a new ARCH model of the specified order.
GarchModel(Vector<Double>, Int32, Int32) Constructs a new GARCH model of the specified order.

Properties

ArchParameters Gets the parameters corresponding to the lagged squared innovations in the model.
BaseFeatureIndex Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model)
Computed Gets whether the model has been computed.
(Inherited from Model)
Obsolete.
ConditionalVariances Gets a vector containing the estimated conditional variances.
Constant Gets the parameter for the constant term in the model.
CovarianceMatrix Gets the covariance matrix of the model parameters.
(Inherited from TimeSeriesModel<T>)
Data Gets an object that contains all the data used as input to the model.
(Inherited from Model)
DegreesOfFreedom Gets the total degrees of freedom of the data.
(Inherited from TimeSeriesModel<T>)
Distribution Gets the conditional distribution of the innovations process. If no value is specified, a standard normal distribution is assumed.
Fitted Gets whether the model has been computed.
(Inherited from Model)
GarchParameters Gets the parameters corresponding to the lagged conditional variances in the model.
InnovationDistribution Gets or sets the probability distribution of the innovations of the GARCH model.
InputSchema Gets the schema for the features used for fitting the model.
(Inherited from Model)
LogLikelihood Gets the log-likelihood that the model generated the data.
(Inherited from TimeSeriesModel<T>)
MaxDegreeOfParallelism Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model)
ModelSchema Gets the collection of variables used in the model.
(Inherited from Model)
NumberOfObservations Gets the number of observations the model is based on.
(Inherited from Model)
P Gets or sets the number of lagged squared innovations in the model.
ParallelOptions Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model)
Parameters Gets the collection of parameters associated with this model.
(Inherited from TimeSeriesModel<T>)
ParameterValues Gets the collection of parameters associated with this model.
(Inherited from TimeSeriesModel<T>)
Predictions Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from TimeSeriesModel<T>)
Q Gets or sets the number of lagged conditional variances in the model.
Residuals Gets a vector containing the residuals of the model.
(Inherited from TimeSeriesModel<T>)
ResidualSumOfSquares Gets the sum of squares of the residuals of the model.
(Inherited from TimeSeriesModel<T>)
StandardError Gets the standard error of the regression.
(Inherited from TimeSeriesModel<T>)
Status Gets the status of the model, which determines which information is available.
(Inherited from Model)
StudentTDegreesOfFreedom Gets or sets the degrees of freedom when fitting a GARCH model with innovations from a student-t distribution.
SupportsWeights Indicates whether the model supports case weights.
(Inherited from Model)
TimeSeries Gets the time series that is being modeled.
(Inherited from TimeSeriesModel<T>)
Weights Gets or sets the actual weights.
(Inherited from Model)

Methods

Compute() Computes the model.
(Inherited from Model)
Obsolete.
Compute(ParallelOptions) Computes the model.
(Inherited from Model)
Obsolete.
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
Fit() Fits the model to the data.
(Inherited from Model)
Fit(ParallelOptions) Fits the model to the data.
(Inherited from Model)
FitCore Computes the model to the specified input using the specified parallelization options.
(Overrides Model.FitCore(ModelInput, ParallelOptions))
Forecast() Returns the one step ahead forecast.
(Inherited from TimeSeriesModel<T>)
Forecast(Int32) Returns the forecast for the specified number of steps ahead.
(Overrides TimeSeriesModel<T>.Forecast(Int32))
Forecast(Int32, Vector<Double>, Vector<Double>) Returns the forecast for the specified number of steps ahead.
GetAkaikeInformationCriterion Returns the Akaike information criterion (AIC) value for the model.
(Inherited from TimeSeriesModel<T>)
GetBayesianInformationCriterion Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from TimeSeriesModel<T>)
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetTypeGets the Type of the current instance.
(Inherited from Object)
MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
ResetComputation Clears all fitted model parameters.
(Inherited from Model)
Obsolete.
ResetFit Clears all fitted model parameters.
(Inherited from Model)
SetDataSource Uses the specified data frame as the source for all input variables.
(Inherited from Model)
Summarize() Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model)
Summarize(SummaryOptions) Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model.Summarize(SummaryOptions))
ToStringReturns a string that represents the current object.
(Inherited from Model)

See Also