(To be removed) Display parameter estimation results for ARIMA or ARIMAX models
Description
print(
displays parameter estimates, standard errors, and t statistics for a
fitted ARIMA or ARIMAX model.EstMdl,EstParamCov)
Examples
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 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
Input Arguments
Estimated error variance-covariance matrix as returned by
estimate, specified as a square matrix with rows and columns
corresponding to parameters known to the optimizer of estimate.
Known parameters include all parameters estimate estimated. Rows
and columns associated with parameters fixed during estimation contain
0s.
The order of the parameters (that is, rows and columns) in
EstParamCov is:
Constant
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
EstMdlcontains 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)
Version History
Introduced in R2012aprint will be removed in a future release. Use summarize
instead.
This list shows the differences between print and
summarize:
For an unestimated (custom)
arimamodel input,summarizereturns the standard object display of the model.For an estimated
arimamodel input, as returned byestimate,summarizeprints an estimation summary in a MATLAB table and lists other estimation statistics.summarizereturns the estimation statistics in an output structure array.
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