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# print

Class: gjr

Display parameter estimation results for GJR models

## Syntax

print(fit,VarCov)

## Description

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

## Input Arguments

 fit Estimated gjr model object, as output by estimate. VarCov 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:ConstantNonzero GARCH coefficients at positive lagsNonzero ARCH coefficients at positive lagsNonzero leverage coefficients at positive lagsDegrees of freedom (t innovation distribution only)Offset (models with nonzero offset only)

## Examples

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,...
'Leverage',0.1);
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.

```print(fit,VarCov)
```
```
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
```