arima

Convert regression model with ARIMA errors to ARIMAX model

Syntax

``ARIMAXMdl = arima(Mdl)``
``````[ARIMAXMdl,XNew] = arima(Mdl,X=X)``````

Description

The `arima` object function converts a specified regression model with ARIMA errors (`regARIMA` model object) to the equivalent ARIMAX model (`arima` model object). To create an ARIMAX model directly, see the `arima` function.

example

````ARIMAXMdl = arima(Mdl)` returns the fully specified `arima` model object `ARIMAXMdl`, which is the ARIMAX model representation of the input regression model with ARIMA time series errors `Mdl`, a fully specified `regARIMA` model object.```

example

``````[ARIMAXMdl,XNew] = arima(Mdl,X=X)``` specifies predictor data `X` for the regression component of the input regression model with ARIMA errors, and returns the transformed predictor data `XNew` for the output ARIMAX model.```

Examples

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Convert a regression model with ARMA(4,1) errors to an ARIMAX model using the `arima` converter.

Specify the regression model with ARMA(4,1) errors:

`$\begin{array}{l}{y}_{t}=1+0.5{X}_{t}+{u}_{t}\\ {u}_{t}=0.8{u}_{t-1}-0.4{u}_{t-4}+{\epsilon }_{t}+0.3{\epsilon }_{t-1},\end{array}$`

where ${\epsilon }_{t}$ is Gaussian with mean 0 and variance 1.

```Mdl = regARIMA(AR={0.8 -0.4},ARLags=[1 4],MA=0.3, ... Intercept=1,Beta=0.5,Variance=1)```
```Mdl = regARIMA with properties: Description: "Regression with ARMA(4,1) Error Model (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: 1 Beta: [0.5] P: 4 Q: 1 AR: {0.8 -0.4} at lags [1 4] SAR: {} MA: {0.3} at lag [1] SMA: {} Variance: 1 ```

You can verify that the lags of the autoregressive terms are `1` and `4` in the `AR` row.

Generate random predictor data.

```rng(1); % For reproducibility T = 20; Pred = randn(T,1);```

Convert `Mdl` to an ARIMAX model. Supply the random set of predictor data `Pred` for `Mdl` and return the predictor data for the converted model.

```[ARIMAXMdl,XNew] = arima(Mdl,X=Pred); ARIMAXMdl```
```ARIMAXMdl = arima with properties: Description: "ARIMAX(4,0,1) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 4 D: 0 Q: 1 Constant: 0.6 AR: {0.8 -0.4} at lags [1 4] SAR: {} MA: {0.3} at lag [1] SMA: {} Seasonality: 0 Beta: [1 -0.8 0.4] Variance: 1 ```

The output `arima` model `ARIMAXMdl` is

`${y}_{t}=0.6+{Z}_{t}\Gamma +0.8{y}_{t-1}-0.4{y}_{t-4}+{\epsilon }_{t}+0.3{\epsilon }_{t-1},$`

where

`${Z}_{t}\Gamma =\left[\begin{array}{ccc}0.5{x}_{1}& NaN& NaN\\ 0.5{x}_{2}& 0.5{x}_{1}& NaN\\ 0.5{x}_{3}& 0.5{x}_{2}& NaN\\ 0.5{x}_{4}& 0.5{x}_{3}& NaN\\ 0.5{x}_{5}& 0.5{x}_{4}& 0.5{x}_{1}\\ ⋮& ⋮& ⋮\\ {0.5}_{T}& 0.5{x}_{T-1}& 0.5{x}_{T-4}\end{array}\right]\left[\begin{array}{c}1\\ -0.8\\ 0.4\end{array}\right]$`

and ${x}_{j}$ is row j of `Pred`. Because the product of the autoregressive and integration polynomials is $\varphi \left(L\right)=\left(1-0.8L+0.4{L}^{4}\right),$ `ARIMAX.Beta` is `[1; -0.8; 0.4]`. Note that the software carries over the autoregressive and moving average coefficients from `Mdl` to `ARIMAX`. Also, `Mdl.Intercept` = 1 and `ARIMAX.Constant` = (1 - 0.8 + 0.4)(1) = 0.6, i.e., the `regARIMA` model intercept and `arima` model constant are generally unequal.

Convert a regression model with seasonal ARIMA errors to an ARIMAX model using the `arima` converter.

Specify the regression model with $ARIMA\left(2,1,1\right)×{\left(1,1,0\right)}_{2}$ errors:

`$\begin{array}{c}{y}_{t}={X}_{t}\left[\begin{array}{c}-2\\ 1\end{array}\right]+{u}_{t}\\ \left(1-0.3L+0.15{L}^{2}\right)\left(1-L\right)\left(1-0.2{L}^{2}\right)\left(1-{L}^{2}\right){u}_{t}=\left(1+0.1L\right){\epsilon }_{t},\end{array}$`

where ${\epsilon }_{t}$ is Gaussian with mean 0 and variance 1.

```Mdl = regARIMA(AR={0.3, -0.15},MA=0.1,ARLags=[1 2], ... SAR=0.2,SARLags=2,Seasonality=2,D=1, ... Intercept=0,Beta=[-2; 1],Variance=1)```
```Mdl = regARIMA with properties: Description: "Regression with ARIMA(2,1,1) Error Model Seasonally Integrated with Seasonal AR(2) (Gaussian Distribution)" Distribution: Name = "Gaussian" Intercept: 0 Beta: [-2 1] P: 7 D: 1 Q: 1 AR: {0.3 -0.15} at lags [1 2] SAR: {0.2} at lag [2] MA: {0.1} at lag [1] SMA: {} Seasonality: 2 Variance: 1 ```

Generate predictor data.

```rng(1); % For reproducibility T = 20; Pred = randn(T,2);```

Convert `Mdl` to an ARIMAX model. Supply the random set of predictor data `Pred` for `Mdl` and return the predictor data for the converted model.

```[ARIMAX,XNew] = arima(Mdl,X=Pred); ARIMAX```
```ARIMAX = arima with properties: Description: "ARIMAX(2,1,1) Model Seasonally Integrated with Seasonal AR(2) (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 7 D: 1 Q: 1 Constant: 0 AR: {0.3 -0.15} at lags [1 2] SAR: {0.2} at lag [2] MA: {0.1} at lag [1] SMA: {} Seasonality: 2 Beta: [1 -1.3 -0.75 1.41 -0.34 -0.08 0.09 -0.03] Variance: 1 ```

`Mdl.Beta` has length 2, but `ARIMAX.Beta` has length 8. This is because the product of the autoregressive and integration polynomials, $\varphi \left(L\right)\left(1-L\right)\Phi \left(L\right)\left(1-{L}^{s}\right)$, is

`$1-1.3L-0.75{L}^{2}+1.41{L}^{3}-0.34{L}^{4}-0.08{L}^{5}+0.09{L}^{6}-0.03{L}^{7}.$`

You can see that when you add seasonality, seasonal lag terms, and integration to a model, the size of `XNew` can grow quite large. A conversion such as this might not be ideal for analyses involving small sample sizes.

Input Arguments

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Fully specified regression model with ARIMA time series errors, specified as a `regARIMA` model object created by `regARIMA` or `estimate`.

Predictor data for the regression component of the input regression model with ARIMA time series errors `Mdl`, specified as a T-by-k1 numeric matrix, where k1 is `numel(Mdl.Beta)`.

The last row of `X` contains the latest observation of each series.

Each column of `X` is a separate time series.

Data Types: `double`

Output Arguments

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ARIMAX model equivalent to the input regression model with ARIMA time series errors `Mdl`, returned as a fully specified model object of type `arima`.

Converted predictor data matrix for the regression component of the output ARIMAX model `ARIMAXMdl`, returned as a T-by-k2 numeric matrix, where k2 is one plus the number of nonzero autoregressive coefficients in the difference equation of `Mdl`. To return `XNew`, you must supply `X`.

The last row of `XNew` contains the latest observation of each series.

Each column of `XNew` is a separate time series.

Data Types: `double`

Algorithms

Let X denote the matrix of concatenated predictor data vectors (or design matrix) and β denote the regression component for the regression model with ARIMA errors, `Mdl`.

• If you specify `X`, `arima` returns `XNew` in a certain format. Suppose that the nonzero autoregressive lag term degrees of `Mdl` are 0 < a1 < a2 < ...< P, which is the largest lag term degree. The software obtains these lag term degrees by expanding and reducing the product of the seasonal and nonseasonal autoregressive lag polynomials, and the seasonal and nonseasonal integration lag polynomials

`$\varphi \left(L\right){\left(1-L\right)}^{D}\Phi \left(L\right)\left(1-{L}^{s}\right).$`

• The first column of `XNew` is .

• The second column of `XNew` is a sequence of a1 `NaN`s, and then the product ${X}_{{a}_{1}}\beta ,$ where ${X}_{{a}_{1}}\beta ={L}^{{a}_{1}}X\beta .$

• Column j of `XNew` is a sequence of aj `NaN`s, and then the product ${X}_{{a}_{j}}\beta ,$ where ${X}_{{a}_{j}}\beta ={L}^{{a}_{j}}X\beta .$

• The last column of `XNew` is a sequence of ap `NaN`s, and then the product ${X}_{p}\beta ,$ where ${X}_{p}\beta ={L}^{p}X\beta .$

Suppose that `Mdl` is a regression model with ARIMA(3,1,0) errors, and ϕ1 = 0.2 and ϕ3 = 0.05. Then the product of the autoregressive and integration lag polynomials is

`$\left(1-0.2L-0.05{L}^{3}\right)\left(1-L\right)=1-1.2L+0.02{L}^{2}-0.05{L}^{3}+0.05{L}^{4}.$`

This implies that `ARIMAXMdl.Beta` is ```[1 -1.2 0.02 -0.05 0.05]``` and `XNew` is

`$\left[\begin{array}{ccccc}{x}_{1}\beta & NaN& NaN& NaN& NaN\\ {x}_{2}\beta & {x}_{1}\beta & NaN& NaN& NaN\\ {x}_{3}\beta & {x}_{2}\beta & {x}_{1}\beta & NaN& NaN\\ {x}_{4}\beta & {x}_{3}\beta & {x}_{2}\beta & {x}_{1}\beta & NaN\\ {x}_{5}\beta & {x}_{4}\beta & {x}_{3}\beta & {x}_{2}\beta & {x}_{1}\beta \\ ⋮& ⋮& ⋮& ⋮& ⋮\\ {x}_{T}\beta & {x}_{T-1}\beta & {x}_{T-2}\beta & {x}_{T-3}\beta & {x}_{T-4}\beta \end{array}\right],$`

where xj is row j of X.

• If you do not specify `X`, `arima` returns `XNew` as an empty matrix without rows and one plus the number of nonzero autoregressive coefficients in the difference equation of `Mdl` columns.

Version History

Introduced in R2013b