For conditional mean model estimation,
arima model and a vector of univariate time series
data. The model specifies the parametric form of the conditional mean
fitted values for any parameters in the input model with
If you pass a
T×r exogenous covariate matrix
X argument, then
estimates . If you specify non-
NaN values for any
estimate views these values as equality
constraints and honors them during estimation.
For example, suppose you are estimating a model without a constant
'Constant',0 in the model you pass
estimate views this
NaN value as an equality constraint, and does
not estimate the constant term.
estimate also honors
all specified equality constraints while estimating parameters without
equality constraints. You can set a subset of regression coefficients
to a constant and estimate the rest. For example, suppose your model
model. If your model has three exogenous
covariates, and you want to estimate two of them and set the other
to one to 5, then specify
model.Beta = [NaN 5 NaN].
estimate optionally returns the variance-covariance
matrix for estimated parameters. The parameter order in this matrix
Nonzero AR coefficients at positive lags (
Nonzero seasonal AR coefficients at positive lags
Nonzero MA coefficients at positive lags (
Nonzero seasonal MA coefficients at positive lags
Regression coefficients (when you specify
Variance parameters (scalar for constant-variance models, vector of additional parameters otherwise)
Degrees of freedom (t innovation distribution only)
If any parameter known to the optimizer has an equality constraint, then the corresponding row and column of the variance-covariance matrix has all 0s.
In addition to user-specified equality constraints,
any AR or MA coefficient with an estimate less than
magnitude equal to 0.