Class: gjr

Display parameter estimation results for GJR models




print(fit,VarCov) displays parameter estimates, standard errors, and t statistics for a fitted GJR model.

Input Arguments


Estimated gjr model object, as output by estimate.


Estimation error variance-covariance matrix, as output by estimate. VarCov is a square matrix with a row and column for each parameter known to the optimizer when model was fit. Known parameters include all parameters estimated as well as all parameters held fixed during optimization. Rows and columns associated with any parameters held fixed contain 0s.

The parameters in VarCov are ordered as follows:

  • Constant

  • Nonzero GARCH coefficients at positive lags

  • Nonzero ARCH coefficients at positive lags

  • Nonzero leverage coefficients at positive lags

  • Degrees of freedom (t innovation distribution only)

  • Offset (models with nonzero offset only)


expand all

Print GJR Estimation Results

Print the results from estimating a GJR model using simulated data.

Simulate data from a GJR(1,1) model with known parameter values.

modSim = gjr('Constant',0.01,'GARCH',0.8,'ARCH',0.14,...
rng 'default';
[V,Y] = simulate(modSim,100);

Fit a GJR(1,1) model to the simulated data, turning off the print display.

model = gjr(1,1);
[fit,VarCov] = estimate(model,Y,'print',false);

Print the estimation results.

    GJR(1,1) Conditional Variance Model:
    Conditional Probability Distribution: Gaussian

                                  Standard          t     
     Parameter       Value          Error       Statistic 
    -----------   -----------   ------------   -----------
     Constant       0.194785      0.254199       0.766271
     GARCH{1}        0.69954       0.11266        6.20928
      ARCH{1}       0.192965     0.0931335        2.07192
  Leverage{1}       0.214988      0.223923         0.9601
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