ERRORMODEL
equationname
distribution < CDF= CDF(options) > ;
The ERRORMODEL statement is the mechanism for specifying the distribution of the residuals. You must specify the dependent/endogenous
variables or general form model name, a tilde (), and then a distribution with its parameters. You can specify the following options:
Options to Specify the Distribution

CAUCHY( <location, scale> )

specifies the Cauchy distribution. This option is supported only for simulation. The arguments correspond to the arguments
of the SAS CDF function that computes the cumulative distribution function (ignoring the random variable argument).

CHISQUARED ( df <, nc> )

specifies the distribution. This option is supported only for simulation. The arguments correspond to the arguments of the SAS CDF function
(ignoring the random variable argument).

GENERAL(Likelihood <, parm1, parm2,
parm
n
> )

specifies the negative of a general loglikelihood function that you construct by using SAS programming statements. The procedure
minimizes the negative loglikelihood function specified. are optional parameters for this distribution and are used for documentation purposes only.

F( ndf, ddf <, nc> )

specifies the distribution. This option is supported only for simulation. The arguments correspond to the arguments of the SAS CDF function
(ignoring the random variable argument).

NORMAL( )

specifies a multivariate normal (Gaussian) distribution with mean and variances through .

POISSON( mean )

specifies the Poisson distribution. This option is supported only for simulation. The arguments correspond to the arguments
of the SAS CDF function (ignoring the random variable argument).

T( , df )

specifies a multivariate distribution with noncentrality , variance through , and common degrees of freedom .

UNIFORM( <left, right> )

specifies the uniform distribution. This option is supported only for simulation. The arguments correspond to the arguments
of the SAS CDF function (ignoring the random variable argument).
Options to Specify the CDF for Simulation

CDF=( CDF(options) )

specifies the univariate distribution that is used for simulation so that the estimation can be done for one set of distributional
assumptions and the simulation for another. The CDF can be any of the distributions from the previous section with the exception of the general likelihood. In addition, you
can specify the empirical distribution of the residuals.

EMPIRICAL= ( <TAILS=(options)> )

uses the sorted residual data to create an empirical CDF.

TAILS=( tailoptions )

specifies how to handle the tails in computing the inverse CDF from an empirical distribution, where tailoptions are:
 NORMAL

specifies the normal distribution to extrapolate the tails.
 T( df )

specifies the distribution to extrapolate the tails.
 PERCENT= p

specifies the percentage of the observations to use in constructing each tail. The default for the PERCENT= option is 10.
A normal distribution or a distribution is used to extrapolate the tails to infinity. The variance for the tail distribution is obtained from the data
so that the empirical CDF is continuous.
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