least absolute deviations regression
[rlad]=lad(namey,arg1,...,argn)
* namey = a time series, a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes
* argi = an argument that can be:
- a time series
- a real (nx1) vector
- a real (nxk) matrix
- a string equal to the name of a time series or a (nxk) real vector or matrix between quotes
- a list of such elements
- the string 'noprint' if the user doesn't want to display the results of the regression
- the string 'itmax=n' where n is the maximum # of iterations (for example 'itmax=10'; default=500)
- the string 'crit=n' where n is the convergence criterion (for example 'crit=1e-5'; default = 1e-15)
* rlad = a tlist with
- rlad('meth') = 'lad'
- rlad('y') = y data vector
- rlad('x') = x data matrix
- rlad('nobs') = nobs
- rlad('nvar') = nvars
- rlad('b_new') = bhat
- rlad('yhat') = yhat
- rlad('resid') = residuals
- rlad('vcovar') = estimated variance-covariance matrix of b_new
- rlad('sige') = estimated variance of the residuals
- rlad('sige') = estimated variance of the residuals
- rlad('ser') = standard error of the regression
- rlad('tstat') = t-stats
- rlad('pvalue') = pvalue of the b_news
- rlad('dw') = Durbin-Watson Statistic
- rlad('prescte') = boolean indicating the presence or absence of a constant in the regression
- rlad('namey') = name of the y variable
- rlad('namex') = name of the x variables
- rlad('bounds') = if there is a timeseries in the regression, the bounds of the regression
- rlad('iter') = # of iterations
- rlad('conv') = convergence max(abs(bnew-bold))
- rlad('weight') = weight used to do the last ols regression