Garch Model 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#
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.3
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
Garch | Constructs a new ARCH model of the specified order. |
Garch | Constructs a new GARCH model of the specified order. |
Garch | Constructs a new ARCH model of the specified order. |
Garch | Constructs a new GARCH model of the specified order. |
Properties
Arch | Gets the parameters corresponding to the lagged squared innovations in the model. |
Base |
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. |
Conditional | Gets a vector containing the estimated conditional variances. |
Constant | Gets the parameter for the constant term in the model. |
Covariance |
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) |
Degrees |
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) |
Garch | Gets the parameters corresponding to the lagged conditional variances in the model. |
Innovation | Gets or sets the probability distribution of the innovations of the GARCH model. |
Input |
Gets the schema for the features used for fitting the model.
(Inherited from Model) |
Log |
Gets the log-likelihood that the model generated the data.
(Inherited from TimeSeriesModel<T>) |
Max |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model) |
Model |
Gets the collection of variables used in the model.
(Inherited from Model) |
Number |
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. |
Parallel |
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>) |
Parameter |
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>) |
Residual |
Gets the sum of squares of the residuals of the model.
(Inherited from TimeSeriesModel<T>) |
Standard |
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) |
Student | Gets or sets the degrees of freedom when fitting a GARCH model with innovations from a student-t distribution. |
Supports |
Indicates whether the model supports case weights.
(Inherited from Model) |
Time |
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( |
Computes the model.
(Inherited from Model) Obsolete. |
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
Finalize | Allows 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( |
Fits the model to the data.
(Inherited from Model) |
Fit |
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( |
Returns the forecast for the specified number of steps ahead.
(Overrides TimeSeriesModel<T>.Forecast(Int32)) |
Forecast( | Returns the forecast for the specified number of steps ahead. |
Get |
Returns the Akaike information criterion (AIC) value for the model.
(Inherited from TimeSeriesModel<T>) |
Get |
Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from TimeSeriesModel<T>) |
Get | Serves as the default hash function. (Inherited from Object) |
Get | Gets the Type of the current instance. (Inherited from Object) |
Memberwise | Creates a shallow copy of the current Object. (Inherited from Object) |
Reset |
Clears all fitted model parameters.
(Inherited from Model) Obsolete. |
Reset |
Clears all fitted model parameters.
(Inherited from Model) |
Set |
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( |
Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model.Summarize(SummaryOptions)) |
ToString | Returns a string that represents the current object. (Inherited from Model) |