White's adjusted heteroscedastic estimation
[rhwhite]=hwhite(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
* arg1 to argn = arguments which 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
* rhwhite = a tlist with
- rhwhite('meth') = 'White''s heteroskedasticity consistent'
-rhwhite('y') = y data vector
-rhwhite('x') = x data matrix
-rhwhite('nobs') = nobs
-rhwhite('nvar') = nvars
-rhwhite('beta') = bhat
-rhwhite('yhat') = yhat
-rhwhite('resid') = residuals
-rhwhite('vcovar') = estimated variance-covariance matrix of beta
-rhwhite('sige') = estimated variance of the residuals
-rhwhite('sige') = estimated variance of the residuals
-rhwhite('ser') = standard error of the regression
-rhwhite('tstat') = t-stats
-rhwhite('pvalue') = pvalue of the betas
-rhwhite('dw') = Durbin-Watson Statistic
-rhwhite('prescte') = boolean indicating the presence or absence of a constant in the regression
-rhwhite('rsqr') = rsquared
-rhwhite('rbar') = rbar-squared
-rhwhite('f') = F-stat for the nullity of coefficients other than the constant
-rhwhite('pvaluef') = its significance level
-rhwhite('prescte') = boolean indicating the presence or absence of a time series in the regression
-rhwhite('namey') = name of the y variable
-rhwhite('namex') = name of the x variables
-rhwhite('bounds') = if there is a timeseries in the regression, the bounds of the regression
load(GROCERDIR+'/data/bdhenderic.dat'); hwhite('del(lm1-lp)','del(lp)','del(lagts(1,lm1-lp-ly))','rnet','lagts(1,lm1-lp-ly)','cte') // example taken from hwhite_d: provides White's adjusted heteroscedastic consistent Least-squares Regression // for Hendry and Ericsson (1991) equation 6 (results not presented by the authors). | ![]() | ![]() |