numeric value. The tolerance for detecting linear dependencies in the columns of the regressors, see <code><a href="/link/qr?package=gets&version=0.2" rd-options="" data-mini-rdoc="gets::qr">qr</a></code> function. Only used if LAPACK is FALSE

logical, TRUE or FALSE (default). If true use LAPACK otherwise use LINPACK, see <code><a href="/link/qr?package=gets&version=0.2" rd-options="" data-mini-rdoc="gets::qr">qr</a></code> function

LAPACK

1 (default) or 2. Method 2 returns slightly more information, which means it is slightly slower. However, the information returned can be used to speed up the computation of variance-covariance matrices

method

Accurate, robust and fast OLS estimation with the QR decomposition

Statistical Models

Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting structural breaks in the mean. The mean can be specified as an autoregressive model with covariates (an 'AR-X' model), and the variance can be specified as a log-variance model with covariates (a 'log-ARCH-X' model). The four main functions of the package are arx, getsm, getsv and isat. The first function, arx, estimates an AR-X model with log-ARCH-X errors. The second function, getsm, undertakes GETS model selection of the mean specification of an arx object. The third function, getsv, undertakes GETS model selection of the log-variance specification of an arx object. The fourth function, isat, undertakes GETS model selection of an indicator saturated mean specification.

ols: OLS estimation with the QR decomposition

Description

Usage

Arguments

Value

See Also