For regression models with ARIMA time series errors in Econometrics Toolbox™, εt = σzt, where:
εt is the innovation corresponding to observation t.
σ is the constant variance of the innovations. You
can set its value using the
Variance property of a
zt is the innovation
distribution. You can set the distribution using the
Distribution property of a
regARIMA model. Specify either a standard
Gaussian (the default) or standardized Student’s t
with ν > 2 or
NaN degrees of
If εt has a Student’s t distribution, then
where Tν is a Student’s t random variable with ν > 2 degrees of freedom. Subsequently, zt is t-distributed with mean 0 and variance 1, but has the same kurtosis as Tν. Therefore, εt is t-distributed with mean 0, variance σ, and has the same kurtosis as Tν.
estimate builds and optimizes the likelihood objective function
based on εt by:
Estimating c and β using MLR
Inferring the unconditional disturbances from the estimated regression model,
Estimating the ARIMA error model, where H(L) is the compound autoregressive polynomial and N(L) is the compound moving average polynomial
Inferring the innovations from the ARIMA error model,
Maximizing the loglikelihood objective function with respect to the free parameters
If the unconditional disturbance process is nonstationary (i.e., the
nonseasonal or seasonal integration degree is greater than 0), then the
regression intercept, c, is not identifiable.
estimate returns a
c when it fits integrated models. For details, see
Intercept Identifiability in Regression Models with ARIMA Errors.
estimate estimates all parameters in the
regARIMA model set to
estimate honors any equality constraints in the
regARIMA model, i.e.,
estimate fixes the
parameters at the values that you set during estimation.
Given its history, the innovations are conditionally independent. Let Ht denote the history of the process available at time t, where t = 1,...,T. The likelihood function of the innovations is
where f is the standard Gaussian or t probability density function.
The exact form of the loglikelihood objective function depends on the parametric form of the innovation distribution.
If zt is standard Gaussian, then the loglikelihood objective function is
If zt is a standardized Student’s t, then the loglikelihood objective function is
estimate performs covariance matrix
estimation for maximum likelihood estimates using the outer product of
gradients (OPG) method.