Specify GJR Models

Default GJR Model

The default GJR(P,Q) model in Econometrics Toolbox™ is of the form

`${\epsilon }_{t}={\sigma }_{t}{z}_{t},$`

with Gaussian innovation distribution and

`${\sigma }_{t}^{2}=\kappa +\sum _{i=1}^{P}{\gamma }_{i}{\sigma }_{t-i}^{2}+\sum _{j=1}^{Q}{\alpha }_{j}{\epsilon }_{t-j}^{2}+\sum _{j=1}^{Q}{\xi }_{j}I\left[{\epsilon }_{t-j}<0\right]{\epsilon }_{t-j}^{2}.$`

The indicator function $I\left[{\epsilon }_{t-j}<0\right]$ equals 1 if ${\epsilon }_{t-j}<0$ and 0 otherwise. The default model has no mean offset, and the lagged variances and squared innovations are at consecutive lags.

You can specify a model of this form using the shorthand syntax `gjr(P,Q)`. For the input arguments `P` and `Q`, enter the number of lagged variances (GARCH terms), P, and lagged squared innovations (ARCH and leverage terms), Q, respectively. The following restrictions apply:

• P and Q must be nonnegative integers.

• If P > 0, then you must also specify Q > 0

When you use this shorthand syntax, `gjr` creates a `gjr` model with these default property values.

PropertyDefault Value
`P`Number of GARCH terms, P
`Q`Number of ARCH and leverage terms, Q
`Offset``0`
`Constant``NaN`
`GARCH`Cell vector of `NaN`s
`ARCH`Cell vector of `NaN`s
`Leverage`Cell vector of `NaN`s
`Distribution``"Gaussian"`

To assign nondefault values to any properties, you can modify the created model using dot notation.

To illustrate, consider specifying the GJR(1,1) model

`${\epsilon }_{t}={\sigma }_{t}{z}_{t},$`

with Gaussian innovation distribution and

`${\sigma }_{t}^{2}=\kappa +{\gamma }_{1}{\sigma }_{t-1}^{2}+{\alpha }_{1}{\epsilon }_{t-1}^{2}+{\xi }_{1}I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}.$`

`Mdl = gjr(1,1)`
```Mdl = gjr with properties: Description: "GJR(1,1) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: NaN GARCH: {NaN} at lag [1] ARCH: {NaN} at lag [1] Leverage: {NaN} at lag [1] Offset: 0 ```

The created model, `Mdl`, has `NaN`s for all model parameters. A `NaN` value signals that a parameter needs to be estimated or otherwise specified by the user. All parameters must be specified to forecast or simulate the model.

To estimate parameters, input the model (along with data) to `estimate`. This returns a new fitted `gjr` model. The fitted model has parameter estimates for each input `NaN` value.

Calling `gjr` without any input arguments returns a GJR(0,0) model specification with default property values:

`DefaultMdl = gjr`
```DefaultMdl = gjr with properties: Description: "GJR(0,0) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 0 Q: 0 Constant: NaN GARCH: {} ARCH: {} Leverage: {} Offset: 0 ```

Specify Default GJR Model

This example shows how to use the shorthand `gjr(P,Q)` syntax to specify the default GJR(P, Q) model, ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$ with a Gaussian innovation distribution and

`${\sigma }_{t}^{2}=\kappa +\sum _{i=1}^{P}{\gamma }_{i}{\sigma }_{t-i}^{2}+\sum _{j=1}^{Q}{\alpha }_{j}{\epsilon }_{t-j}^{2}+\sum _{j=1}^{Q}{\xi }_{j}I\left[{\epsilon }_{t-j}<0\right]{\epsilon }_{t-j}^{2}.$`

By default, all parameters in the created model have unknown values.

Specify the default GJR(1,1) model:

`Mdl = gjr(1,1)`
```Mdl = gjr with properties: Description: "GJR(1,1) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: NaN GARCH: {NaN} at lag [1] ARCH: {NaN} at lag [1] Leverage: {NaN} at lag [1] Offset: 0 ```

The output shows that the created model, `Mdl`, has `NaN` values for all model parameters: the constant term, the GARCH coefficient, the ARCH coefficient, and the leverage coefficient. You can modify the created model using dot notation, or input it (along with data) to `estimate`.

Using Name-Value Arguments

The most flexible way to specify GJR models is using name-value arguments. You do not need, nor are you able, to specify a value for every model property. `gjr` assigns default values to any model properties you do not (or cannot) specify.

The general GJR(P,Q) model is of the form

`${y}_{t}=\mu +{\epsilon }_{t},$`

where ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$ and

`${\sigma }_{t}^{2}=\kappa +\sum _{i=1}^{P}{\gamma }_{i}{\sigma }_{t-i}^{2}+\sum _{j=1}^{Q}{\alpha }_{j}{\epsilon }_{t-j}^{2}+\sum _{j=1}^{Q}{\xi }_{j}I\left[{\epsilon }_{t-j}<0\right]{\epsilon }_{t-j}^{2}.$`

The innovation distribution can be Gaussian or Student’s t. The default distribution is Gaussian.

In order to estimate, forecast, or simulate a model, you must specify the parametric form of the model (e.g., which lags correspond to nonzero coefficients, the innovation distribution) and any known parameter values. You can set any unknown parameters equal to `NaN`, and then input the model to `estimate` (along with data) to get estimated parameter values.

`gjr` (and `estimate`) returns a model corresponding to the model specification. You can modify models to change or update the specification. Input models (with no `NaN` values) to `forecast` or `simulate` for forecasting and simulation, respectively. Here are some example specifications using name-value arguments.

ModelSpecification
• ${y}_{t}={\epsilon }_{t}$

• ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$

• zt Gaussian

• ${\sigma }_{t}^{2}=\kappa +{\gamma }_{1}{\sigma }_{t-1}^{2}+{\alpha }_{1}{\epsilon }_{t-1}^{2}+{\xi }_{1}I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}$

```gjr('GARCH',NaN,'ARCH',NaN,... 'Leverage',NaN)``` or `gjr(1,1)`
• ${y}_{t}=\mu +{\epsilon }_{t}$

• ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$

• zt Student’s t with unknown degrees of freedom

• ${\sigma }_{t}^{2}=\kappa +{\gamma }_{1}{\sigma }_{t-1}^{2}+{\alpha }_{1}{\epsilon }_{t-1}^{2}+{\xi }_{1}I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}$

```gjr('Offset',NaN,'GARCH',NaN,... 'ARCH',NaN,'Leverage',NaN,... 'Distribution','t')```
• ${y}_{t}={\epsilon }_{t}$

• ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$

• zt Student’s t with eight degrees of freedom

• ${\sigma }_{t}^{2}=0.1+0.6{\sigma }_{t-1}^{2}+0.3{\epsilon }_{t-1}^{2}+0.05I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}$

```gjr('Constant',0.1,'GARCH',0.6,... 'ARCH',0.3,'Leverage',0.05,... 'Distribution',... struct('Name','t','DoF',8))```

Here is a full description of the name-value arguments you can use to specify GJR models.

Note

You cannot assign values to the properties `P` and `Q`. `egarch` sets `P` equal to the largest GARCH lag, and `Q` equal to the largest lag with a nonzero squared innovation coefficient, including ARCH and leverage coefficients.

Name-Value Arguments for GJR Models

NameCorresponding GJR Model Term(s)When to Specify
`Offset`Mean offset, μTo include a nonzero mean offset. For example, `'Offset',0.2`. If you plan to estimate the offset term, specify `'Offset',NaN`.
By default, `Offset` has value `0` (meaning, no offset).
`Constant`Constant in the conditional variance model, κTo set equality constraints for κ. For example, if a model has known constant 0.1, specify `'Constant',0.1`.
By default, `Constant` has value `NaN`.
`GARCH`GARCH coefficients, ${\gamma }_{1},\dots ,{\gamma }_{P}$To set equality constraints for the GARCH coefficients. For example, to specify a GJR(1,1) model with ${\gamma }_{1}=0.6,$ specify `'GARCH',0.6`.
You only need to specify the nonzero elements of `GARCH`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `GARCHLags`.
Any coefficients you specify must satisfy all stationarity constraints.
`GARCHLags`Lags corresponding to the nonzero GARCH coefficients`GARCHLags` is not a model property.
Use this argument as a shortcut for specifying `GARCH` when the nonzero GARCH coefficients correspond to nonconsecutive lags. For example, to specify nonzero GARCH coefficients at lags 1 and 3, e.g., nonzero ${\gamma }_{1}$ and ${\gamma }_{3},$ specify `'GARCHLags',[1,3]`.
Use `GARCH` and `GARCHLags` together to specify known nonzero GARCH coefficients at nonconsecutive lags. For example, if ${\gamma }_{1}=0.3$ and ${\gamma }_{3}=0.1,$ specify `'GARCH',{0.3,0.1},'GARCHLags',[1,3]`
`ARCH`ARCH coefficients, ${\alpha }_{1},\dots ,{\alpha }_{Q}$To set equality constraints for the ARCH coefficients. For example, to specify a GJR(1,1) model with ${\alpha }_{1}=0.3,$ specify `'ARCH',0.3`.
You only need to specify the nonzero elements of `ARCH`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `ARCHLags`.
`ARCHLags`Lags corresponding to nonzero ARCH coefficients

`ARCHLags` is not a model property.

Use this argument as a shortcut for specifying `ARCH` when the nonzero ARCH coefficients correspond to nonconsecutive lags. For example, to specify nonzero ARCH coefficients at lags 1 and 3, e.g., nonzero ${\alpha }_{1}$ and ${\alpha }_{3},$ specify `'ARCHLags',[1,3]`.

Use `ARCH` and `ARCHLags` together to specify known nonzero ARCH coefficients at nonconsecutive lags. For example, if ${\alpha }_{1}=0.4$ and ${\alpha }_{3}=0.2,$ specify `'ARCH',{0.4,0.2},'ARCHLags',[1,3]`

`Leverage`Leverage coefficients, ${\xi }_{1},\dots ,{\xi }_{Q}$

To set equality constraints for the leverage coefficients. For example, to specify a GJR(1,1) model with ${\xi }_{1}=0.1$ specify `'Leverage',0.1`.

You only need to specify the nonzero elements of `Leverage`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `LeverageLags`.

`LeverageLags`Lags corresponding to nonzero leverage coefficients

`LeverageLags` is not a model property.

Use this argument as a shortcut for specifying `Leverage` when the nonzero leverage coefficients correspond to nonconsecutive lags. For example, to specify nonzero leverage coefficients at lags 1 and 3, e.g., nonzero ${\xi }_{1}$ and ${\xi }_{3},$ specify `'LeverageLags',[1,3]`.

Use `Leverage` and `LeverageLags` together to specify known nonzero leverage coefficients at nonconsecutive lags. For example, if ${\xi }_{1}=0.1$ and ${\xi }_{3}=0.05,$ specify `'Leverage',{0.1,0.05},'LeverageLags',[1,3]`.

`Distribution`Distribution of the innovation process

Use this argument to specify a Student’s t innovation distribution. By default, the innovation distribution is Gaussian.

For example, to specify a t distribution with unknown degrees of freedom, specify `'Distribution','t'`.

To specify a t innovation distribution with known degrees of freedom, assign `Distribution` a data structure with fields `Name` and `DoF`. For example, for a t distribution with nine degrees of freedom, specify `'Distribution',struct('Name','t','DoF',9)`.

Specify GJR Model Using Econometric Modeler App

You can specify the lag structure, innovation distribution, and leverages of GJR models using the Econometric Modeler app. The app treats all coefficients as unknown and estimable, including the degrees of freedom parameter for a t innovation distribution.

At the command line, open the Econometric Modeler app.

`econometricModeler`

Alternatively, open the app from the apps gallery (see Econometric Modeler).

In the app, you can see all supported models by selecting a time series variable for the response in the Time Series pane. Then, on the Econometric Modeler tab, in the Models section, click the arrow to display the models gallery.

The GARCH Models section contains all supported conditional variance models. To specify a GJR model, click `GJR`. The GJR Model Parameters dialog box appears.

• GARCH Degree – The order of the GARCH polynomial.

• ARCH Degree – The order of the ARCH polynomial. The value of this parameter also specifies the order of the leverage polynomial.

• Include Offset – The inclusion of a model offset.

• Innovation Distribution – The innovation distribution.

As you adjust parameter values, the equation in the Model Equation section changes to match your specifications. Adjustable parameters correspond to input and name-value pair arguments described in the previous sections and in the `gjr` reference page.

For more details on specifying models using the app, see Fitting Models to Data and Specifying Univariate Lag Operator Polynomials Interactively.

Specify GJR Model with Mean Offset

This example shows how to specify a GJR(P, Q) model with a mean offset. Use name-value pair arguments to specify a model that differs from the default model.

Specify a GJR(1,1) model with a mean offset,

`${y}_{t}=\mu +{\epsilon }_{t},$`

where ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$ and

`${\sigma }_{t}^{2}=\kappa +{\gamma }_{1}{\sigma }_{t-1}^{2}+{\alpha }_{1}{\epsilon }_{t-1}^{2}+{\xi }_{1}I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}.$`

```Mdl = gjr('Offset',NaN,'GARCHLags',1,'ARCHLags',1,... 'LeverageLags',1)```
```Mdl = gjr with properties: Description: "GJR(1,1) Conditional Variance Model with Offset (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: NaN GARCH: {NaN} at lag [1] ARCH: {NaN} at lag [1] Leverage: {NaN} at lag [1] Offset: NaN ```

The mean offset appears in the output as an additional parameter to be estimated or otherwise specified.

Specify GJR Model with Nonconsecutive Lags

This example shows how to specify a GJR model with nonzero coefficients at nonconsecutive lags.

Specify a GJR(3,1) model with nonzero GARCH terms at lags 1 and 3. Include a mean offset.

```Mdl = gjr('Offset',NaN,'GARCHLags',[1,3],'ARCHLags',1,... 'LeverageLags',1)```
```Mdl = gjr with properties: Description: "GJR(3,1) Conditional Variance Model with Offset (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 3 Q: 1 Constant: NaN GARCH: {NaN NaN} at lags [1 3] ARCH: {NaN} at lag [1] Leverage: {NaN} at lag [1] Offset: NaN ```

The unknown nonzero GARCH coefficients correspond to lagged variances at lags 1 and 3. The output shows only the nonzero coefficients.

Display the value of `GARCH`:

`Mdl.GARCH`
```ans=1×3 cell array {[NaN]} {[0]} {[NaN]} ```

The `GARCH` cell array returns three elements. The first and third elements have value `NaN`, indicating these coefficients are nonzero and need to be estimated or otherwise specified. By default, `gjr` sets the interim coefficient at lag 2 equal to zero to maintain consistency with MATLAB® cell array indexing.

Specify GJR Model with Known Parameter Values

This example shows how to specify a GJR model with known parameter values. You can use such a fully specified model as an input to `simulate` or `forecast`.

Specify the GJR(1,1) model

`${\sigma }_{t}^{2}=0.1+0.6{\sigma }_{t-1}^{2}+0.2{\epsilon }_{t-1}^{2}+0.1I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}$`

with a Gaussian innovation distribution.

```Mdl = gjr('Constant',0.1,'GARCH',0.6,'ARCH',0.2,... 'Leverage',0.1)```
```Mdl = gjr with properties: Description: "GJR(1,1) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: 0.1 GARCH: {0.6} at lag [1] ARCH: {0.2} at lag [1] Leverage: {0.1} at lag [1] Offset: 0 ```

Because all parameter values are specified, the created model has no `NaN` values. The functions `simulate` and `forecast` don't accept input models with `NaN` values.

Specify GJR Model with t Innovation Distribution

This example shows how to specify a GJR model with a Student's t innovation distribution.

Specify a GJR(1,1) model with a mean offset,

`${y}_{t}=\mu +{\epsilon }_{t},$`

where ${\epsilon }_{t}={\sigma }_{t}{z}_{t}$ and

`${\sigma }_{t}^{2}=\kappa +{\gamma }_{1}{\sigma }_{t-1}^{2}+{\alpha }_{1}{\epsilon }_{t-1}^{2}+{\xi }_{1}I\left[{\epsilon }_{t-1}<0\right]{\epsilon }_{t-1}^{2}.$`

Assume ${z}_{t}$ follows a Student's t innovation distribution with 10 degrees of freedom.

```tDist = struct('Name','t','DoF',10); Mdl = gjr('Offset',NaN,'GARCHLags',1,'ARCHLags',1,... 'LeverageLags',1,'Distribution',tDist)```
```Mdl = gjr with properties: Description: "GJR(1,1) Conditional Variance Model with Offset (t Distribution)" SeriesName: "Y" Distribution: Name = "t", DoF = 10 P: 1 Q: 1 Constant: NaN GARCH: {NaN} at lag [1] ARCH: {NaN} at lag [1] Leverage: {NaN} at lag [1] Offset: NaN ```

The value of `Distribution` is a `struct` array with field `Name` equal to `'t'` and field `DoF` equal to `10`. When you specify the degrees of freedom, they aren't estimated if you input the model to `estimate`.