## Create Regression Models with AR Errors

These examples show how to create regression models with AR errors using `regARIMA`

. For details on specifying regression models with AR errors using
the **Econometric
Modeler** app, see Specify Regression Model with ARMA Errors Using Econometric Modeler App.

### Default Regression Model with AR Errors

This example shows how to apply the shorthand `regARIMA(p,D,q)`

syntax to specify a regression model with AR errors.

Specify the default regression model with AR(3) errors:

$$\begin{array}{l}{y}_{t}=c+{X}_{t}\beta +{u}_{t}\\ {u}_{t}={a}_{1}{u}_{t-1}+{a}_{2}{u}_{t-2}+{a}_{3}{u}_{t-3}+{\epsilon}_{t}.\end{array}$$

Mdl = regARIMA(3,0,0)

Mdl = regARIMA with properties: Description: "ARMA(3,0) Error Model (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: NaN Beta: [1×0] P: 3 Q: 0 AR: {NaN NaN NaN} at lags [1 2 3] SAR: {} MA: {} SMA: {} Variance: NaN

The software sets the innovation distribution to `Gaussian`

, and each parameter to `NaN`

. The AR coefficients are at lags 1 through 3.

Pass `Mdl`

into `estimate`

with data to estimate the parameters set to `NaN`

. Though `Beta`

is not in the display, if you pass a matrix of predictors ($${X}_{t}$$) into `estimate`

, then `estimate`

estimates `Beta`

. The `estimate`

function infers the number of regression coefficients in `Beta`

from the number of columns in $${X}_{t}$$.

Tasks such as simulation and forecasting using `simulate`

and `forecast`

do not accept models with at least one `NaN`

for a parameter value. Use dot notation to modify parameter values.

### AR Error Model Without an Intercept

This example shows how to specify a regression model with AR errors without a regression intercept.

Specify the default regression model with AR(3) errors:

$$\begin{array}{l}{y}_{t}={X}_{t}\beta +{u}_{t}\\ {u}_{t}={a}_{1}{u}_{t-1}+{a}_{2}{u}_{t-2}+{a}_{3}{u}_{t-3}+{\epsilon}_{t}.\end{array}$$

Mdl = regARIMA('ARLags',1:3,'Intercept',0)

Mdl = regARIMA with properties: Description: "ARMA(3,0) Error Model (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: 0 Beta: [1×0] P: 3 Q: 0 AR: {NaN NaN NaN} at lags [1 2 3] SAR: {} MA: {} SMA: {} Variance: NaN

The software sets `Intercept`

to 0, but all other estimable parameters in `Mdl`

are `NaN`

values by default.

Since `Intercept`

is not a `NaN`

, it is an equality constraint during estimation. In other words, if you pass `Mdl`

and data into `estimate`

, then `estimate`

sets `Intercept`

to 0 during estimation.

You can modify the properties of `Mdl`

using dot notation.

### AR Error Model with Nonconsecutive Lags

This example shows how to specify a regression model with AR errors, where the nonzero AR terms are at nonconsecutive lags.

Specify the regression model with AR(4) errors:

$$\begin{array}{l}{y}_{t}=c+{X}_{t}\beta +{u}_{t}\\ {u}_{t}={a}_{1}{u}_{t-1}+{a}_{4}{u}_{t-4}+{\epsilon}_{t}.\end{array}$$

`Mdl = regARIMA('ARLags',[1,4])`

Mdl = regARIMA with properties: Description: "ARMA(4,0) Error Model (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: NaN Beta: [1×0] P: 4 Q: 0 AR: {NaN NaN} at lags [1 4] SAR: {} MA: {} SMA: {} Variance: NaN

The AR coefficients are at lags 1 and 4.

Verify that the AR coefficients at lags 2 and 3 are 0.

Mdl.AR

`ans=`*1×4 cell array*
{[NaN]} {[0]} {[0]} {[NaN]}

The software displays a 1-by-4 cell array. Each consecutive cell contains the corresponding AR coefficient value.

Pass `Mdl`

and data into `estimate`

. The software estimates all parameters that have the value `NaN`

. Then, `estimate`

holds $${a}_{2}$$ = 0 and $${a}_{3}$$ = 0 during estimation.

### Known Parameter Values for a Regression Model with AR Errors

This example shows how to specify values for all parameters of a regression model with AR errors.

Specify the regression model with AR(4) errors:

$$\begin{array}{l}{y}_{t}={X}_{t}\left[\begin{array}{l}-2\\ 0.5\end{array}\right]+{u}_{t}\\ {u}_{t}=0.2{u}_{t-1}+0.1{u}_{t-4}+{\epsilon}_{t},\end{array}$$

where $${\epsilon}_{t}$$ is Gaussian with unit variance.

Mdl = regARIMA('AR',{0.2,0.1},'ARLags',[1,4], ... 'Intercept',0,'Beta',[-2;0.5],'Variance',1)

Mdl = regARIMA with properties: Description: "Regression with ARMA(4,0) Error Model (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: 0 Beta: [-2 0.5] P: 4 Q: 0 AR: {0.2 0.1} at lags [1 4] SAR: {} MA: {} SMA: {} Variance: 1

There are no `NaN`

values in any `Mdl`

properties, and therefore there is no need to estimate `Mdl`

using `estimate`

. However, you can simulate or forecast responses from `Mdl`

using `simulate`

or `forecast`

.

### Regression Model with AR Errors and t Innovations

This example shows how to set the innovation distribution of a regression model with AR errors to a $$t$$ distribution.

Specify the regression model with AR(4) errors:

$$\begin{array}{l}{y}_{t}={X}_{t}\left[\begin{array}{l}-2\\ 0.5\end{array}\right]+{u}_{t}\\ {u}_{t}=0.2{u}_{t-1}+0.1{u}_{t-4}+{\epsilon}_{t},\end{array}$$

where $${\epsilon}_{t}$$ has a $$t$$ distribution with the default degrees of freedom and unit variance.

Mdl = regARIMA('AR',{0.2,0.1},'ARLags',[1,4],... 'Intercept',0,'Beta',[-2;0.5],'Variance',1,... 'Distribution','t')

Mdl = regARIMA with properties: Description: "Regression with ARMA(4,0) Error Model (t Distribution)" Distribution: Name = "t", DoF = NaN Intercept: 0 Beta: [-2 0.5] P: 4 Q: 0 AR: {0.2 0.1} at lags [1 4] SAR: {} MA: {} SMA: {} Variance: 1

The default degrees of freedom is `NaN`

. If you don't know the degrees of freedom, then you can estimate it by passing `Mdl`

and the data to `estimate`

.

Specify a $${t}_{10}$$ distribution.

Mdl.Distribution = struct('Name','t','DoF',10)

Mdl = regARIMA with properties: Description: "Regression with ARMA(4,0) Error Model (t Distribution)" Distribution: Name = "t", DoF = 10 Intercept: 0 Beta: [-2 0.5] P: 4 Q: 0 AR: {0.2 0.1} at lags [1 4] SAR: {} MA: {} SMA: {} Variance: 1

You can simulate or forecast responses using `simulate`

or `forecast`

because `Mdl`

is completely specified.

In applications, such as simulation, the software normalizes the random $$t$$ innovations. In other words, `Variance`

overrides the theoretical variance of the $$t$$ random variable (which is `DoF`

/(`DoF`

- 2)), but preserves the kurtosis of the distribution.

## See Also

### Apps

### Objects

### Functions

## Related Examples

- Analyze Time Series Data Using Econometric Modeler
- Specifying Univariate Lag Operator Polynomials Interactively
- Create Regression Models with ARIMA Errors
- Specify the Default Regression Model with ARIMA Errors
- Create Regression Models with MA Errors
- Create Regression Models with ARMA Errors
- Create Regression Models with SARIMA Errors
- Specify ARIMA Error Model Innovation Distribution