robust regression
[rrobust]=robust(wfunc,wparm,grocer_namey, arg1,...,argn)
* wfunc = 'huber' for Huber's t function
-'ramsay' for Ramsay's E function
-'andrew' for Andrew's wave function
-'tukey' for Tukey's biweight
* wparm = weighting function parameter
* grocer_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
* rrobust = a tlist with
- rrobust('meth') = 'robust'+ 'huber', 'ramsay', 'andrew' or 'tukey'
- rrobust('y') = y data vector
- rrobust('x') = x data matrix
- rrobust('nobs') = nobs
- rrobust('nvar') = nvars
- rrobust('beta') = bhat
- rrobust('yhat') = yhat
- rrobust('resid') = residuals
- rrobust('vcovar') = estimated variance-covariance matrix of beta
- rrobust('sige') = estimated variance of the residuals
- rrobust('sige') = estimated variance of the residuals
- rrobust('ser') = standard error of the regression
- rrobust('tstat') = t-stats
- rrobust('pvalue') = pvalue of the betas
- rrobust('dw') = Durbin-Watson Statistic
- rrobust('prescte') = boolean indicating the presence or absence of a constant in the regression
- rrobust('rsqr') = rsquared
- rrobust('rbar') = rbar-squared
- rrobust('f') = F-stat for the nullity of coefficients other than the constant
- rrobust('pvaluef') = its significance level
- rrobust('prescte') = boolean indicating the presence or absence of a time series in the regression
- rrobust('namey') = name of the y variable
- rrobust('namex') = name of the x variables
- rrobust('bounds') = if there is a timeseries in the regression, the bounds of the regression
- rrobust('wparm') = wparm
- rrobust('iter') = # of iterations
- rrobust('weight') = nobs - vector of weights
- rrobust('convg') = convg criterion
load('grocer/bdexamples/bdhenderic.dat') ; bounds('1964q3','1989q2') ; r = robust('huber', 0.000338','delts(lm1-lp)','delts(lp)','delts(lagts(1,lm1-lp-ly))','rnet', 'lagts(1,lm1-lp-ly)','cte') r = robust('andrew', 0.000338','delts(lm1-lp)','delts(lp)','delts(lagts(1,lm1-lp-ly))','rnet', 'lagts(1,lm1-lp-ly)','cte', 'noprint') // These examples shows the results of a robust regression on Hendry and Ericsson's preferred regression, using huber's weighting scheme in example 1 and andrew's one in example 2. Results are not displayed in example 2. | ![]() | ![]() |