# 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 0s. 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.

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