# Documentation

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# print

Class: arima

Display parameter estimation results for ARIMA or ARIMAX models

## 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,'print',false); 

Print the estimation results.

print(EstMdl,EstParamCov) 
 ARIMA(1,0,1) Model: -------------------- Conditional Probability Distribution: Gaussian Standard t Parameter Value Error Statistic ----------- ----------- ------------ ----------- Constant 0.0445374 0.0460376 0.967413 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 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,'print',false); 

Print the estimation results.

 print(EstMdl,EstParamCov) 
 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 -0.0306019 Beta1 0.0239329 0.0218417 1.09574 Beta2 -0.0124602 0.00749917 -1.66154 Beta3 0.0680871 0.0745041 0.91387 Variance 0.00539463 0.00224393 2.4041