# print

(To be removed) Display parameter estimation results for ARIMA or ARIMAX models

`print` will be removed in a future release. Use `summarize` instead.

## Syntax

```print(EstMdl,EstParamCov) ```

## Description

`print(EstMdl,EstParamCov)` displays parameter estimates, standard errors, and t statistics for a fitted ARIMA or ARIMAX model.

## Input Arguments

 `EstMdl` `arima` model estimated using `estimate`. `EstParamCov` Estimation error variance-covariance matrix, as output by `estimate`. `EstParamCov` is a square matrix with a row and column for each parameter known to the optimizer when `Mdl` was fit by `estimate`. Known parameters include all parameters `estimate` estimated. If you specified a parameter as fixed during estimation, then it is also a known parameter and the rows and columns associated with it contain `0`s. The parameters in `EstParamCov` are ordered as follows: ConstantNonzero AR coefficients at positive lagsNonzero SAR coefficients at positive lagsNonzero MA coefficients at positive lagsNonzero SMA coefficients at positive lagsRegression coefficients (when `EstMdl` contains them)Variance parameters (scalar for constant-variance models, or a vector of parameters for a conditional variance model)Degrees of freedom (t innovation distribution only)

## Examples

expand all

Print the results from estimating an ARIMA model using simulated data.

Simulate data from an ARMA(1,1) model using known parameter values.

```MdlSim = arima('Constant',0.01,'AR',0.8,'MA',0.14,... 'Variance',0.1); rng 'default'; Y = simulate(MdlSim,100);```

Fit an ARMA(1,1) model to the simulated data, turning off the print display.

```Mdl = arima(1,0,1); [EstMdl,EstParamCov] = estimate(Mdl,Y,'Display','off'); ```

Print the estimation results.

`print(EstMdl,EstParamCov) `
```Warning: PRINT will be removed in a future release; use SUMMARIZE instead. ```
``` ARIMA(1,0,1) Model: -------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant 0.0445373 0.0460376 0.967412 AR{1} 0.822892 0.0711631 11.5635 MA{1} 0.12032 0.101817 1.18173 Variance 0.133727 0.0178793 7.4794 ```

Print the results of estimating an ARIMAX model.

Load the Credit Defaults data set, assign the response IGD to `Y` and the predictors AGE, CPF, and SPR to the matrix `X`, and obtain the sample size `T`. To avoid distraction from the purpose of this example, assume that all predictor series are stationary.

```load Data_CreditDefaults X = Data(:,[1 3:4]); T = size(X,1); y = Data(:,5);```

Separate the initial values from the main response and predictor series.

```y0 = y(1); yEst = y(2:T); XEst = X(2:end,:);```

Set the ARIMAX(1,0,0) model ${y}_{t}=c+{\varphi }_{1}{y}_{t-1}+{\epsilon }_{t}$ to `MdlY` to fit to the data.

`MdlY = arima(1,0,0);`

Fit the model to the data and specify the initial values.

```[EstMdl,EstParamCov] = estimate(MdlY,yEst,'X',XEst,... 'Y0',y0,'Display','off');```

Print the estimation results.

` print(EstMdl,EstParamCov) `
```Warning: PRINT will be removed in a future release; use SUMMARIZE instead. ```
``` ARIMAX(1,0,0) Model: --------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant -0.204768 0.266078 -0.769578 AR{1} -0.017309 0.565618 -0.030602 Beta(1) 0.0239329 0.0218417 1.09574 Beta(2) -0.0124602 0.00749917 -1.66154 Beta(3) 0.0680871 0.0745041 0.91387 Variance 0.00539463 0.00224393 2.4041 ```