quantile regression estimation
res = qreg(namey,tau,varargin);
* 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
* tau = a (q x 1) vector, the values of the quantiles
* arg1,...,argn = arguments which can be:
- a time series
- a real (nxp) vector
- a string equal to the name of a time series or a (nxp) real vector between quotes
- the string 'noprint' if the user doesn't want to print the results of the regression
- 'dropna' if the user wants to remove the NA values from the data
- 'algo=xxx' where xxx is the alogrithm used to find the solution ('linpro' or 'qreg_solvelp1')
- 'weight=xxx' where xxx is a vector of the same size as the endoegnous variable, if the user wants to weight differently the observations (default: equal weights)
- 'maxit=xxx' where xxx is the maximum number of iterations allowed (default: none)
- 'sigma=xxx' where xxx is a scalar, < 1, the scaling factor determines how close the corrector step is allowed to come to the boundary of the constraint set in the interior point method
- 'eps=xxx' where xxx is the tolerance value for convergence (default: sqrt(%eps))
- 'big=xxx' where xxx is the number used to remove the residuals of the wrong sign (default: 1E20)
* res = a results tlist with
- res('meth') = 'quantile'
- res('y') = y data vector
- res('x') = x data matrix
- res('tau') = vectores of quantiles to be estimated
- res('weights') = 0 or a (nobs x 1) vector of observations weights
- res('nobs') = # observations
- res('nvar') = # variables
- res('beta') = (nvar x q) matrix of quantile estimations
- res('prests') = boolean indicating the presence or absence of a time series in the regression
- res('namey') = name of the y variable
- res('namex') = name of the x variables
- res('dropna') = boolean indicating if NAs have been dropped
- res('bounds') = if there is a timeseries in the regression, the bounds of the regression
- res('nonna') = vector indicating position of non-NAs
load(GROCERDIR+'/data\qreg_d.dat') france_clim_t=m2q((france_clim_c+lagts(france_clim_c)+lagts(2,france_clim_c))/3,2) france_clim_nl=delts(france_clim_t)*abs(delts(france_clim_t)) rqreg=qreg('growthr(FRA_GDP)',[0.05;0.95],'const','france_clim_t','france_clim_nl','algo=linpro') // Performs quantile regression on a "bridge" model relating French GDP growth to the French business climate taken from business surveys. | ![]() | ![]() |