Tobit estimation
[rtobit]=tobit(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,...,argn = arguments which can be:
- a time series
- a real (nx1) vector
- a string equal to the name of a time series or a (nx1) real vector between quotes
- a string option which can be:
- 'trunc=left' or 'trunc=right' for censoring (default=left)
- 'vtrunc=x' where x is the value for censoring (default=0)
- 'b0=x' where x is the starting values for parameters (default = ols)
- 'hess=x' where x = Hessian: 'dfp', 'bfgs', 'gn', 'marq', 'sd' (default = 'bfgs')
- 'btol=x' where x is the tolerance for b convergence (default = 1e-8)
- 'ftol=x' tolerance for FUN convergence (default = 1e-8)
- 'maxit=x' is the maximum # of iterations (default = 500)
* rtobit = a results tlist with
- rtobit('meth') = 'tobit'
- rtobit('y') = y data vector
- rtobit('x') = x data matrix
- rtobit('nobs') = # observations
- rtobit('nobsc') = # censored observations
- rtobit('nvar') = # variables
- rtobit('beta') = bhat
- rtobit('yhat') = yhat
- rtobit('resid') = residuals
- rtobit('vcovar') = estimated variance-covariance matrix of beta
- rtobit('sige') = estimated variance of the residuals
- rtobit('sigu') = sum of squared residuals
- rtobit('ser') = standard error of the regression
- rtobit('tstat') = t-stats
- rtobit('pvalue') = pvalue of the betas
- rtobit('dw') = Durbin-Watson Statistic
- rtobit('condindex') = multicolinearity cond index
- rtobit('prescte') = %f = boolean indicating the absence of a constant in the regression
- rtobit('iter') = # iterations performed
- rtobit('llike') = log likelihood
- rtobit('opthess') = option used to update hessian
- rtobit('grad') = gradient at the optimum
- rtobit('ts') = boolean indicating the presence or absence of a time series in the regression
- rtobit('namey') = name of the y variable
- rtobit('namex') = name of the x variables
- rtobit('bounds') = if there is a timeseries in the regression, the bounds of the regression
xr = grand(200,5,'nor',0,1); bet = ones(5,1); yr = xr*bet+rand(200,1,'n'); // now censor the data for i = 1:200 if yr(i,1)<0 then yr(i,1) = 0; end end // an example which provide only the results without // printing them r = tobit('yr','xr'); // Example taken from tobit_d. The endogenous varaible is y, x is a matrix of exogenous variables. Hessian calcualtion method is dfp and maximum // # of iterations is set to 1000. | ![]() | ![]() |