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print

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

Description

example

print(EstMdl,EstParamCov) displays parameter estimates, standard errors, and t statistics of a fitted regression model with ARIMA model.

Examples

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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(1,0,1);
[EstMdl,EstParamCov] = estimate(Mdl,dlogGDP,X=dCPI,Display="off");

Print the estimates.

print(EstMdl,EstParamCov)
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        6.77872
        MA{1}      -0.161651       0.10956       -1.47546
        Beta1     0.00204403   0.000706163        2.89456
     Variance    9.35782e-05   6.03135e-06        15.5153

Input Arguments

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Estimated regression model with ARIMA errors, specified as a regARIMA model object returned by estimate.

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

EstParamCov is a square matrix with a row and column for each parameter known to the optimizer that estimate uses to fit EstMdl. Known parameters include all parameters estimate estimates. If you specify equality constraints on a parameter for estimation, the parameter is known and the rows and columns associated with it contain zeros.

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

print arranges 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

Version History

Introduced in R2013b

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See Also

Objects

Functions