# recursive: Recursive estimation In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

## Description

Recursive estimation of coefficients and standard errors

## Usage

 ```1 2``` ```recursive(object, spec="mean", std.errors=TRUE, from=40, tol=1e-07, LAPACK=FALSE, plot=TRUE, return=TRUE) ```

## Arguments

 `object` an `arx`, `gets` or `isat` object `spec` 'mean' or 'variance'. If 'mean' (default), the the recursive estimates of the mean-equation are estimated `std.errors` logical. If TRUE (default), then the coefficient standard errors are also computed `from` integer. The starting point of the recursion `tol` numeric. The tolerance for linear dependency among regressors `LAPACK` logical, TRUE or FALSE (default). If true use LAPACK otherwise use LINPACK, see `qr` function `plot` NULL or logical. If TRUE, then the recursive coefficient estimates are plotted. If NULL (default), then the value set by `options` determines whether a plot is produced or not. `return` logical. If TRUE (default), then the recursive estimates are returned in a list

## Value

If `return=TRUE`, then a `list` is returned with the following components:

 `estimates` a `zoo` matrix with the recursive estimates `standard.errors` a `zoo` matrix with the standard errors

## Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

`ols`, `qr`, `solve.qr`
 ```1 2 3 4 5 6 7``` ```##generate random variates, estimate model: y <- rnorm(100) mX <- matrix(rnorm(4*100), 100, 4) mymodel <- arx(y, mc=TRUE, mxreg=mX) ##compute recursive estimates and plot them: recursive(mymodel) ```