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Class: regARIMA

(To be removed) Display estimation results for regression models with ARIMA errors

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




print(Mdl,ParamCov) displays parameter estimates, standard errors, and t statistics for the fitted regression model with ARIMA time series errors Mdl.

Input Arguments

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Regression model with ARIMA errors, specified as a regARIMA model returned by regARIMA or estimate.

Estimation error variance-covariance, specified as a numeric matrix.

ParamCov is a square matrix with a row and column for each parameter known to the optimizer that estimate uses to fit Mdl. Known parameters include all parameters estimate estimates. If you specify a parameter as fixed during estimation, then it is also a known parameter and the rows and columns associated with it contain 0s.

print omits coefficients of lag operator polynomials at lags excluded from Mdl.

print orders the parameters in ParamCov as follows:

  • Intercept

  • Nonzero AR coefficients at positive lags

  • Nonzero SAR coefficients at positive lags

  • Nonzero MA coefficients at positive lags

  • Nonzero SMA coefficients at positive lags

  • Regression coefficients (when Mdl contains them)

  • Variance

  • Degrees of freedom for the t-distribution

Data Types: double


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Regress GDP onto CPI using a regression model with ARMA(1,1) errors, and print the results.

Load the US Macroeconomic data set and preprocess the data.

load Data_USEconModel;
logGDP = log(DataTable.GDP);
dlogGDP = diff(logGDP);
dCPI = diff(DataTable.CPIAUCSL);

Fit the model to the data.

Mdl = regARIMA('ARLags',1,'MALags',1);
[EstMdl,EstParamCov] = estimate(Mdl,dlogGDP,'X',...

Print the estimates.

Warning: PRINT will be removed in a future release; use SUMMARIZE instead.
    Regression with ARIMA(1,0,1) Error Model:
    Conditional Probability Distribution: Gaussian

                                  Standard          t     
     Parameter       Value          Error       Statistic 
    -----------   -----------   ------------   -----------
    Intercept       0.014776    0.00146271        10.1018
        AR{1}       0.605274     0.0892902        6.77873
        MA{1}      -0.161651       0.10956       -1.47546
        Beta1     0.00204403   0.000706163        2.89456
     Variance    9.35782e-05   6.03135e-06        15.5153

See Also