sim.sur

This function generates a random sample from an Seemingly Unrelated Regression model.

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others).
'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
Evaluations are both in-sample and out-of-sample.
It is designed to be efficient in terms of CPU usage and memory consumption.

Ramin Mojab

Automated Uncertainty Analysis

Stephen Becker

sim.sur function

<dl><dt>sigma</dt>
<dd>covariance matrix of the errors.
If it is an integer value, it specifies the number of equations in the SUR model and covariance matrix is generated randomly.</dd>
<dt>coef</dt>
<dd>Coefficients of the model.
If it is an integer value, it specifies the number of exogenous variables in each equation of the SUR model and coefficient matrix is generated randomly.</dd>
<dt>nObs</dt>
<dd>Number of observations to generate.</dd>
<dt>intercept</dt>
<dd>If <code>TRUE</code>, an intercept is included in the model as the first exogenous variable.</dd></dl>

Arguments

Generate Random Sample from an SUR Model — sim.sur

<dl>

<dt>sigma</dt>
<dd>covariance matrix of the errors.
If it is an integer value, it specifies the number of equations in the SUR model and covariance matrix is generated randomly.</dd>


<dt>coef</dt>
<dd>Coefficients of the model.
If it is an integer value, it specifies the number of exogenous variables in each equation of the SUR model and coefficient matrix is generated randomly.</dd>


<dt>nObs</dt>
<dd>Number of observations to generate.</dd>


<dt>intercept</dt>
<dd>If <code>TRUE</code>, an intercept is included in the model as the first exogenous variable.</dd>

</dl>

sim.sur: Generate Random Sample from an SUR Model

Description

Usage

Value

Arguments

See Also

Examples