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This example shows how to use the shorthand `arima(p,D,q)`

syntax to specify the default ARMA(*p*, *q*) model,

$${y}_{t}=6+0.2{y}_{t-1}-0.3{y}_{t-2}+3{x}_{t}+{\epsilon}_{t}+0.1{\epsilon}_{t-1}$$

By default, all parameters in the created model object have unknown values, and the innovation distribution is Gaussian with constant variance.

Specify the default ARMA(1,1) model:

Mdl = arima(1,0,1)

Mdl = arima with properties: Description: "ARIMA(1,0,1) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 1 D: 0 Q: 1 Constant: NaN AR: {NaN} at lag [1] SAR: {} MA: {NaN} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: NaN

The output shows that the created model object, Mdl, has `NaN`

values for all model parameters: the constant term, the AR and MA coefficients, and the variance. You can modify the created model object using dot notation, or input it (along with data) to `estimate`

.

This example shows how to specify an ARMA(*p*, *q*) model with constant term equal to zero. Use name-value syntax to specify a model that differs from the default model.

Specify an ARMA(2,1) model with no constant term,

$${y}_{t}={\varphi}_{1}{y}_{t-1}+{\varphi}_{2}{y}_{t-2}+{\epsilon}_{t}+{\theta}_{1}{\epsilon}_{t-1},$$

where the innovation distribution is Gaussian with constant variance.

Mdl = arima('ARLags',1:2,'MALags',1,'Constant',0)

Mdl = arima with properties: Description: "ARIMA(2,0,1) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 2 D: 0 Q: 1 Constant: 0 AR: {NaN NaN} at lags [1 2] SAR: {} MA: {NaN} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: NaN

The `ArLags`

and `MaLags`

name-value pair arguments specify the lags corresponding to nonzero AR and MA coefficients, respectively. The property `Constant`

in the created model object is equal to `0`

, as specified. The model has default values for all other properties, including `NaN`

values as placeholders for the unknown parameters: the AR and MA coefficients, and scalar variance.

You can modify the created model using dot notation, or input it (along with data) to `estimate`

.

This example shows how to specify an ARMA(*p*, *q*) model with known parameter values. You can use such a fully specified model as an input to `simulate`

or `forecast`

.

Specify the ARMA(1,1) model

$${y}_{t}=0.3+0.7\varphi {y}_{t-1}+{\epsilon}_{t}+0.4{\epsilon}_{t-1},$$

where the innovation distribution is Student's *t* with 8 degrees of freedom, and constant variance 0.15.

tdist = struct('Name','t','DoF',8); Mdl = arima('Constant',0.3,'AR',0.7,'MA',0.4,... 'Distribution',tdist,'Variance',0.15)

Mdl = arima with properties: Description: "ARIMA(1,0,1) Model (t Distribution)" Distribution: Name = "t", DoF = 8 P: 1 D: 0 Q: 1 Constant: 0.3 AR: {0.7} at lag [1] SAR: {} MA: {0.4} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: 0.15

All parameter values are specified, that is, no object property is `NaN`

-valued.

In the **Econometric Modeler** app, you can specify the lag structure, presence of a constant, and innovation distribution of an ARMA(*p*,*q*) model by following these steps. All specified coefficients are unknown but estimable parameters.

At the command line, open the

**Econometric Modeler**app.econometricModeler

Alternatively, open the app from the apps gallery (see

**Econometric Modeler**).In the

**Time Series**pane, select the response time series to which the model will be fit.On the

**Econometric Modeler**tab, in the**Models**section, click**ARMA**.The

**ARMA Model Parameters**dialog box appears.Specify the lag structure. To specify an ARMA(

*p*,*q*) model that includes all AR lags from 1 through*p*and all MA lags from 1 through*q*, use the**Lag Order**tab. For the flexibility to specify the inclusion of particular lags, use the**Lag Vector**tab. For more details, see Specifying Lag Operator Polynomials Interactively. Regardless of the tab you use, you can verify the model form by inspecting the equation in the**Model Equation**section.

For example:

To specify an ARMA(2,1) model that includes a constant, includes all AR and MA lags from 1 through their respective orders, and has a Gaussian innovation distribution:

Set

**Autoregressive Order**to`2`

.Set

**Moving Average Order**to`1`

.

To specify an ARMA(2,1) model that includes all AR and MA lags from 1 through their respective orders, has a Gaussian distribution, but does not include a constant:

Set

**Autoregressive Order**to`2`

.Set

**Moving Average Order**to`1`

.Clear the

**Include Constant Term**check box.

To specify an ARMA(2,1) model that includes all AR and MA lags from 1 through their respective orders, includes a constant term, and has

*t*-distributed innovations:Set

**Autoregressive Order**to`2`

.Set

**Moving Average Order**to`1`

.Click the

**Innovation Distribution**button, then select`t`

.

The degrees of freedom parameter of the

*t*distribution is an unknown but estimable parameter.To specify an ARMA(8,4) model containing nonconsecutive lags

$${y}_{t}-{\varphi}_{1}{y}_{t-1}-{\varphi}_{4}{y}_{t-4}-{\varphi}_{8}{y}_{t-8}={\epsilon}_{t}+{\theta}_{1}{\epsilon}_{t-1}+{\theta}_{4}{\epsilon}_{t-4},$$

where

*ε*is a series of IID Gaussian innovations:_{t}Click the

**Lag Vector**tab.Set

**Autoregressive Lags**to`1 4 8`

.Set

**Moving Average Lags**to`1 4`

.Clear the

**Include Constant Term**check box.

After you specify a model, click **Estimate** to estimate all unknown parameters in the model.

- Econometric Modeler App Overview
- Specifying Lag Operator Polynomials Interactively
- Specify Conditional Mean Models
- Modify Properties of Conditional Mean Model Objects
- Specify Conditional Mean Model Innovation Distribution